Comprehensive Guide Feature Engineering

The Comprehensive Guide for Feature Engineering

Posted on Posted in Data Analysis Resources, Machine Learning

Feature Engineering is the art/science of representing data is the best way possible. This is the comprehensive guide for Feature Engineering for myself  but I figured that they might be of interest to some of the blog readers too. Comments on what is written below are most welcome!

Good Feature Engineering

involves an elegant blend of domain knowledge, intuition, and basic mathematical abilities.When you do feature engineering, you are essentially converting your data attributes into data features.The way you present your data to your algorithm should denote the pertinent structures/properties of the underlying information in the most effective way possible.

In Feature engineering you pre-process data so that your model/learning algorithm has to spend minimum effort on wading through the noise. Noise is any information that is not relevant to learning/predicting your ultimate goal. In fact, using good features can even let you use considerably simpler models since you are doing a part of the thinking yourself.

A great place to study examples of feature engineering is in the results from competitive machine learning.

These notes are from link,link,link,link,link ,photo and following link.

Iterative Process of Feature Engineering

The process might look as follows:

  1. Brainstorm features: Really get into the problem, look at a lot of data, study feature engineering on other problems and see what you can steal.
  2. Devise features: Depends on your problem, but you may use automatic feature extraction, manual feature construction and mixtures of the two.
  3. Select features: Use different feature importance scorings and feature selection methods to prepare one or more “views” for your models to operate upon.
  4. Evaluate models: Estimate model accuracy on unseen data using the chosen features.

Few simple Techniques:

Representing timestamps

If you suspect there are relationships between times and other attributes, you can decompose a date-time into constituent parts that may allow models to discover and exploit these relationships.

Time-stamp attributes are usually denoted by the EPOCH time or split up into multiple dimensions such as (Year, Month, Date, Hours, Minutes, Seconds). But in many applications, a lot of that information is unnecessary. Consider for example a supervised system that tries to predict traffic levels in a city as a function of Location+Time. In this case, trying to learn trends that vary by seconds would mostly be misleading. The year wouldn’t add much value to the model as well. Hours, day and month are probably the only dimensions you need. So when representing the time, try to ensure that your model does require all the numbers you are providing it.

Here is an example hypothesis: An applicant who takes days to fill in an application form is likely to be less interested / motivated in the product compared to some one who fills in the same application with in 30 minutes. Similarly, for a bank, time elapsed between dispatch of login details for Online portal and customer logging in might show customers’ willingness to use Online portal. Another example is that a customer living closer to a bank branch is more likely to have a higher engagement than a customer living far off.

And not to forget Time Zones. If your data sources come from different geographical sources, do remember to normalize by time-zones if needed.

A lot of businesses face some kind of seasonality. It could be driven by tax benefits, festive season or weather. If this is the case, you need to make sure that the data and variables are chosen for the right period. For more information

Missing Value Treatment

  1. Deletion: It is of two types: List Wise Deletion and Pair Wise Deletion.
    • In list wise deletion, we delete observations where any of the variable is missing. Simplicity is one of the major advantage of this method, but this method reduces the power of model because it reduces the sample size.
    • In pair wise deletion, we perform analysis with all cases in which the variables of interest are present. Advantage of this method is, it keeps as many cases available for analysis. One of the disadvantage of this method, it uses different sample size for different variables.
    • Deletion methods are used when the nature of missing data is “Missing completely at random” else non random missing values can bias the model output.
  2. Mean/ Mode/ Median Imputation: Imputation is a method to fill in the missing values with estimated ones. The objective is to employ known relationships that can be identified in the valid values of the data set to assist in estimating the missing values. Mean / Mode / Median imputation is one of the most frequently used methods. It consists of replacing the missing data for a given attribute by the mean or median (quantitative attribute) or mode (qualitative attribute) of all known values of that variable. It can be of two types:-
    • Generalized Imputation: In this case, we calculate the mean or median for all non missing values of that variable then replace missing value with mean or median. Like in above table, variable “Manpower” is missing so we take average of all non missing values of “Manpower” (28.33) and then replace missing value with it.
    • Similar case Imputation: In this case, we calculate average for gender “Male” (29.75) and “Female” (25) individually of non missing values then replace the missing value based on gender. For “Male“, we will replace missing values of manpower with 29.75 and for “Female” with 25.
  3. Prediction Model: Prediction model is one of the sophisticated method for handling missing data. Here, we create a predictive model to estimate values that will substitute the missing data. In this case, we divide our data set into two sets: One set with no missing values for the variable and another one with missing values. First data set become training data set of the model while second data set with missing values is test data set and variable with missing values is treated as target variable. Next, we create a model to predict target variable based on other attributes of the training data set and populate missing values of test data set.We can use regression, ANOVA, Logistic regression and various modeling technique to perform this. There are 2 drawbacks for this approach:
    • The model estimated values are usually more well-behaved than the true values
    • If there are no relationships with attributes in the data set and the attribute with missing values, then the model will not be precise for estimating missing values.
  4. KNN Imputation: In this method of imputation, the missing values of an attribute are imputed using the given number of attributes that are most similar to the attribute whose values are missing. The similarity of two attributes is determined using a distance function. It is also known to have certain advantage & disadvantages.
    • Advantages:
      • k-nearest neighbour can predict both qualitative & quantitative attributes
      • Creation of predictive model for each attribute with missing data is not required
      • Attributes with multiple missing values can be easily treated
      • Correlation structure of the data is taken into consideration
    • Disadvantage:
      • KNN algorithm is very time-consuming in analyzing large database. It searches through all the dataset looking for the most similar instances.
      • Choice of k-value is very critical. Higher value of k would include attributes which are significantly different from what we need whereas lower value of k implies missing out of significant attributes.

Create new ratios and proportions

Instead of just keeping past inputs and outputs in your dataset, creating ratios out of them might add a lot of value. Some of the ratios, I have used in past are: Input / Output (past performance), productivity, efficiency and percentages. For example, in order to predict future performance of credit card sales of a branch, ratios like credit card sales / Sales person or Credit card Sales / Marketing spend would be more powerful than just using absolute number of card sold in the branch

Binning/Bucketing

Sometimes, it makes more sense to represent a numerical attribute as a categorical one. The idea is to reduce the noise endured by the learning algorithm, by assigning certain ranges of a numerical attribute to distinct ‘buckets’. Consider the problem of predicting whether a person owns a certain item of clothing or not. Age might definitely be a factor here. What is actually more pertinent, is the Age Group. So what you could do, is have ranges such as 1-10, 11-18, 19-25, 26-40, etc. Moreover, instead of decomposing these categories as in point 2, you could just use scalar values, since age groups that lie ‘closer by’ do represent similar properties.

Bucketing makes sense when the domain of your attribute can be divided into neat ranges, where all numbers falling in a range imply a common characteristic. It reduces overfitting in certain applications, where you don’t want your model to try and distinguish between values that are too close by – for example, you could club together all latitude values that fall in a city, if your property of interest is a function of the city as a whole. Binning also reduces the effect of tiny errors, by ’rounding off’ a given value to the nearest representative. Binning does not make sense if the number of your ranges is comparable to the total possible values, or if precision is very important to you.

Reframe Numerical Quantities

Your data is very likely to contain quantities, which can be reframed to better expose relevant structures. This may be a transform into a new unit or the decomposition of a rate into time and amount components.

You may have a quantity like a weight, distance or timing. A linear transform may be useful to regression and other scale dependent methods.

For example, you may have Item_Weight in grams, with a value like 6289. You could create a new feature with this quantity in kilograms as 6.289 or rounded kilograms like 6. If the domain is shipping data, perhaps kilograms is sufficient or more useful (less noisy) a precision for Item_Weight.

The Item_Weight could be split into two features: Item_Weight_Kilograms and Item_Weight_Remainder_Grams, with example values of 6 and 289 respectively.

There may be domain knowledge that items with a weight above 4 incur a higher taxation rate. That magic domain number could be used to create a new binary feature Item_Above_4kg with a value of “1” for our example of 6289 grams.

You may also have a quantity stored as a rate or an aggregate quantity for an interval. For example, Num_Customer_Purchases aggregated over a year.

In this case you may want to go back to the data collection step and create new features in addition to this aggregate and try to expose more temporal structure in the purchases, like perhaps seasonality. For example, the following new binary features could be created: Purchases_Summer, Purchases_Fall, Purchases_Winter and Purchases_Spring.

Variable Transformation

  • Transform complex non-linear relationships into linear relationships.Existence of a linear relationship between variables is easier to comprehend compared to a non-linear or curved relation. Transformation helps us to convert a non-linear relation into linear relation. Scatter plot can be used to find the relationship between two continuous variables. These transformations also improve the prediction. Log transformation is one of the commonly used transformation technique used in these situations
  • Symmetric distribution is preferred over skewed distribution as it is easier to interpret and generate inferences. Some modeling techniques requires normal distribution of variables. So, whenever we have a skewed distribution, we can use transformations which reduce skewness. For right skewed distribution, we take square / cube root or logarithm of variable and for left skewed, we take square / cube or exponential of variables.
    • Cube root can be applied to negative values including zero. Square root can be applied to positive values including zero.

Outlier Detection and Treatment

Outlier is an observation that appears far away and diverges from an overall pattern in a sample.

Outlier can be of two types: Univariate and Multivariate.

  • Univariate outliers can be found when we look at distribution of a single variable.
  • Multi-variate outliers are outliers in an n-dimensional space. In order to find them, you have to look at distributions in multi-dimensions.

Various types of outliers:

  • Data Entry Errors:- Human errors such as errors caused during data collection, recording, or entry can cause outliers in data. For example: Annual income of a customer is Euros 100,000. Accidentally, the data entry operator puts an additional zero in the figure. Now the income becomes Euros 1,000,000 which is 10 times higher. Evidently, this will be the outlier value when compared with rest of the population.
  • Measurement Error: It is the most common source of outliers. This is caused when the measurement instrument used turns out to be faulty. For example: There are 10 weighing machines. 9 of them are correct, 1 is faulty. Weight measured by people on the faulty machine will be higher / lower than the rest of people in the group. The weights measured on faulty machine can lead to outliers.
  • Experimental Error: Another cause of outliers is experimental error. For example: In a 100m sprint of 7 runners, one runner missed out on concentrating on the ‘Go’ call which caused him to start late. Hence, this caused the runner’s run time to be more than other runners. His total run time can be an outlier.
  • Intentional Outlier: This is commonly found in self-reported measures that involves sensitive data. For example: Teens would typically under report the amount of alcohol that they consume. Only a fraction of them would report actual value. Here actual values might look like outliers because rest of the teens are under reporting the consumption.
  • Data Processing Error: Whenever we perform data mining, we extract data from multiple sources. It is possible that some manipulation or extraction errors may lead to outliers in the dataset.
  • Sampling error: For instance, we have to measure the height of athletes. By mistake, we include a few basketball players in the sample. This inclusion is likely to cause outliers in the dataset.
  • Natural Outlier: When an outlier is not artificial (due to error), it is a natural outlier. For instance: In my last assignment with one of the renowned insurance company, I noticed that the performance of top 50 financial advisors was far higher than rest of the population. Surprisingly, it was not due to any error. Hence, whenever we perform any data mining activity with advisors, we used to treat this segment separately.

Most of the ways to deal with outliers are similar to the methods of missing values like deleting observations, transforming them, binning them, treat them as a separate group, imputing values and other statistical methods.

  • Deleting observations: We delete outlier values if it is due to data entry error, data processing error or outlier observations are very small in numbers. We can also use trimming at both ends to remove outliers.
  • Transforming and binning values: Transforming variables can also eliminate outliers. Natural log of a value reduces the variation caused by extreme values. Binning is also a form of variable transformation. Decision Tree algorithm allows to deal with outliers well due to binning of variable. We can also use the process of assigning weights to different observations.
  • Imputing: Like imputation of missing values, we can also impute outliers. We can use mean, median, mode imputation methods. Before imputing values, we should analyse if it is natural outlier or artificial. If it is artificial, we can go with imputing values. We can also use statistical model to predict values of outlier observation and after that we can impute it with predicted values.
  • Treat separately: If there are significant number of outliers, we should treat them separately in the statistical model. One of the approach is to treat both groups as two different groups and build individual model for both groups and then combine the output.

Decomposing Categorical Attributes

A simple example would be a ‘color’ attribute that is (say) one of {Red, Green, Blue}. The most common way to go about representing this, is to convert each category into a binary attribute that takes one value out of {0, 1}. So you basically end up with a number of added attributes equal to the number of categories possible. And for each instance in your dataset, only one of them is 1 (with the others being 0). This is a form of one-hot encoding.

If you are new to this idea, you may think of decomposition as an unnecessary hassle (we are essentially bloating up the dimensionality of the dataset). Instead, you might be tempted to convert the categorical attribute into a scalar value. For example, the color feature might take one value from {1, 2, 3}, representing {Red, Green, Blue} respectively. There are two problems with this. First, for a mathematical model, this would mean that Red is somehow ‘more similar’ to Green than Blue (since |1-3| > |1-2|). Unless your categories do have a natural ordering (such as stations on a railway line), this might mislead your model. Secondly, it would make statistical metrics (such as mean) meaningless – or worse, misleading yet again. Consider the color example again. If your dataset contains equal numbers of Red and Blue instances but no Green ones, the ‘average’ value of color might still come out to be ~2 – essentially meaning Green!

  • The safest place to convert a categorical attribute into a scalar, is when you have only two categories possible. So you have {0, 1} corresponding to {Category 1, Category 2}. In this case, an ‘ordering’ isn’t really required, and you can interpret the value of the attribute as the probability of belonging to Category 2 vs Category 1.
  • Creating dummy variables: One of the most common application of dummy variable is to convert categorical variable into numerical variables. Dummy variables are also called Indicator Variables. It is useful to take categorical variable as a predictor in statistical models. Categorical variable can take values 0 and 1.
  • Sklearn provides a very efficient tool for encoding the levels of a categorical features into numeric values. LabelEncoder encode labels with value between 0 and n_classes-1.
  • One-Hot Encoding:One-Hot Encoding transforms each categorical feature with n possible values into n binary features, with only one active.Most of the ML algorithms either learn a single weight for each feature or it computes distance between the samples. Algorithms like linear models (such as logistic regression) belongs to the first category.

Encode all the categorical features in Python

In [18]:
# Importing LabelEncoder and initializing it
from sklearn.preprocessing import LabelEncoder
le=LabelEncoder()
# Iterating over all the common columns in train and test
for col in X_test.columns.values:
       # Encoding only categorical variables
       if X_test[col].dtypes=='object':
       # Using whole data to form an exhaustive list of levels
           data=X_train[col].append(X_test[col])
           le.fit(data.values)
           X_train[col]=le.transform(X_train[col])
           X_test[col]=le.transform(X_test[col])
In [20]:
X_train.head(2)
Out[20]:
Loan_ID Gender Married Dependents Education Self_Employed ApplicantIncome CoapplicantIncome LoanAmount Loan_Amount_Term Credit_History Property_Area
0 13 1 0 0 0 0 4950 0.0 125 360 1 2
1 193 1 1 1 0 0 2882 1843.0 123 480 1 1

Before : Male 318 Female 66 Name: Gender, dtype: int64

After : 1 318 0 66 Name: Gender, dtype: int64

One Hot Encoding in Python

In [21]:
from sklearn.preprocessing import OneHotEncoder
enc=OneHotEncoder(sparse=False)
X_train_1=X_train
X_test_1=X_test
columns=['Gender', 'Married', 'Dependents', 'Education','Self_Employed',
          'Credit_History', 'Property_Area']
for col in columns:
       # creating an exhaustive list of all possible categorical values
       data=X_train[[col]].append(X_test[[col]])
       enc.fit(data)
       # Fitting One Hot Encoding on train data
       temp = enc.transform(X_train[[col]])
       # Changing the encoded features into a data frame with new column names
       temp=pd.DataFrame(temp,columns=[(col+"_"+str(i)) for i in data[col]
            .value_counts().index])
       # In side by side concatenation index values should be same
       # Setting the index values similar to the X_train data frame
       temp=temp.set_index(X_train.index.values)
       # adding the new One Hot Encoded varibales to the train data frame
       X_train_1=pd.concat([X_train_1,temp],axis=1)
       # fitting One Hot Encoding on test data
       temp = enc.transform(X_test[[col]])
       # changing it into data frame and adding column names
       temp=pd.DataFrame(temp,columns=[(col+"_"+str(i)) for i in data[col]
            .value_counts().index])
       # Setting the index for proper concatenation
       temp=temp.set_index(X_test.index.values)
       # adding the new One Hot Encoded varibales to test data frame
       X_test_1=pd.concat([X_test_1,temp],axis=1)

Feature Crosses

Feature crosses are a unique way to combine two or more categorical attributes into a single one. This is extremely useful a technique, when certain features together denote a property better than individually by themselves. Mathematically speaking, you are doing a cross product between all possible values of the categorical features.

Consider a feature A, with two possible values {A1, A2}. Let B be a feature with possibilities {B1, B2}. Then, a feature-cross between A & B (lets call it AB) would take one of the following values: {(A1, B1), (A1, B2), (A2, B1), (A2, B2)}. You can basically give these ‘combinations’ any names you like. Just remember that every combination denotes a synergy between the information contained by the corresponding values of A and B.

All the blue points belong to one class, and the red ones belong to another. Lets put the actual model aside. First off, you would benefit from binning the X, Y values into {x < 0, x >= 0} & {y < 0, y >= 0} respectively. Lets call them {Xn, Xp} and {Yn, Yp}. It is pretty obvious that Quadrants I & III correspond to class Red, and Quadrants II & IV contain class Blue. So if you could now cross features X and Y into a single feature ‘Quadrant’, you would basically have {I, II, III, IV} being equivalent to {(Xp, Yp), (Xn, Yp), (Xn, Yn), (Xp, Yn)} respectively.

A more concrete/relatable example of a (possibly) good feature cross is something like (Latitude, Longitude). A common Latitude corresponds to so many places around the globe. Same goes for the Longitude. But once you combine Lat & Long buckets into discreet ‘blocks’, they denote ‘regions’ in a geography, with possibly similar properties for each one.

Sometimes, attributes can also be ‘combined’ into a single feature with simple mathematical hacks. In the above example, suppose you define modified features X{sign} and Y{sign} as follows:

X_{sign} = \frac{x}{|x|}

Y_{sign} = \frac{y}{|y|} link

Now, you could just define a new feature Quadrant_{odd} as follows:

Quadrant{odd} = X{sign}Y_{sign}

Thats all! If Quadrant_{odd} = 1, the class is Red. Else, Blue!

Feature Importance:

An estimate of the usefulness of a feature.

This can be helpful as a pre-cursor to selecting features. Features are allocated scores and can then be ranked by their scores. Those features with the highest scores can be selected for inclusion in the training dataset, whereas those remaining can be ignored.

Feature importance scores can also provide you with information that you can use to extract or construct new features, similar but different to those that have been estimated to be useful.

A feature may be important if it is highly correlated with the dependent variable (the thing being predicted). Correlation coefficients and other univariate (each attribute is considered independently) methods are common methods.

More complex predictive modeling algorithms perform feature importance and selection internally while constructing their model. Some examples include MARS, Random Forest and Gradient Boosted Machines. These models can also report on the variable importance determined during the model preparation process.

Methods that use ensembles of decision trees (like Random Forest or Extra Trees) can also compute the relative importance of each attribute. These importance values can be used to inform a feature selection process.

In [4]:
# Feature Importance
from sklearn import datasets
from sklearn import metrics
from sklearn.ensemble import ExtraTreesClassifier
# load the iris datasets
dataset = datasets.load_iris()
# fit an Extra Trees model to the data
model = ExtraTreesClassifier()
model.fit(dataset.data, dataset.target)
# display the relative importance of each attribute
print(model.feature_importances_)
[ 0.06419645  0.03145001  0.34995716  0.55439638]

Feature Selection :

Using certain algorithms to automatically select a subset of your original features, for your final model. Here, you are not creating/modifying your current features, but rather pruning them to reduce noise/redundancy.

Feature selection algorithms may use a scoring method to rank and choose features, such as correlation or other feature importance methods.

More advanced methods may search subsets of features by trial and error, creating and evaluating models automatically in pursuit of the objectively most predictive sub-group of features.

There are also methods that bake in feature selection or get it as a side effect of the model. Stepwise regression is an example of an algorithm that automatically performs feature selection as part of the model construction process.

Regularization methods like LASSO and ridge regression may also be considered algorithms with feature selection baked in, as they actively seek to remove or discount the contribution of features as part of the model building process.

Feature Selection Algorithms

There are three general classes of feature selection algorithms: filter methods, wrapper methods and embedded methods.

Filter Methods

Filter feature selection methods apply a statistical measure to assign a scoring to each feature. The features are ranked by the score and either selected to be kept or removed from the dataset. The methods are often univariate and consider the feature independently, or with regard to the dependent variable.

Example of some filter methods include the Chi squared test, information gain and correlation coefficient scores.

Wrapper Methods

Wrapper methods consider the selection of a set of features as a search problem, where different combinations are prepared, evaluated and compared to other combinations. A predictive model us used to evaluate a combination of features and assign a score based on model accuracy.

The search process may be methodical such as a best-first search, it may stochastic such as a random hill-climbing algorithm, or it may use heuristics, like forward and backward passes to add and remove features.

An example if a wrapper method is the recursive feature elimination algorithm.

Embedded Methods

Embedded methods learn which features best contribute to the accuracy of the model while the model is being created. The most common type of embedded feature selection methods are regularization methods.

Regularization methods are also called penalization methods that introduce additional constraints into the optimization of a predictive algorithm (such as a regression algorithm) that bias the model toward lower complexity (less coefficients).

Examples of regularization algorithms are the LASSO, Elastic Net and Ridge Regression.

Feature Selection Checklist

http://jmlr.csail.mit.edu/papers/volume3/guyon03a/guyon03a.pdf

  1. Do you have domain knowledge? If yes, construct a better set of ad hoc”” features
  2. Are your features commensurate? If no, consider normalizing them.
  3. Do you suspect interdependence of features? If yes, expand your feature set by constructing conjunctive features or products of features, as much as your computer resources allow you.
  4. Do you need to prune the input variables (e.g. for cost, speed or data understanding reasons)? If no, construct disjunctive features or weighted sums of feature
  5. Do you need to assess features individually (e.g. to understand their influence on the system or because their number is so large that you need to do a first filtering)? If yes, use a variable ranking method; else, do it anyway to get baseline results.
  6. Do you need a predictor? If no, stop
  7. Do you suspect your data is “dirty” (has a few meaningless input patterns and/or noisy outputs or wrong class labels)? If yes, detect the outlier examples using the top ranking variables obtained in step 5 as representation; check and/or discard them.
  8. Do you know what to try first? If no, use a linear predictor. Use a forward selection method with the “probe” method as a stopping criterion or use the 0-norm embedded method for comparison, following the ranking of step 5, construct a sequence of predictors of same nature using increasing subsets of features. Can you match or improve performance with a smaller subset? If yes, try a non-linear predictor with that subset.
  9. Do you have new ideas, time, computational resources, and enough examples? If yes, compare several feature selection methods, including your new idea, correlation coefficients, backward selection and embedded methods. Use linear and non-linear predictors. Select the best approach with model selection
  10. Do you want a stable solution (to improve performance and/or understanding)? If yes, subsample your data and redo your analysis for several “bootstrap”.

Feature Selection in Python

Two different feature selection methods provided by the scikit-learn Python library are Recursive Feature Elimination and feature importance ranking.

Recursive Feature Elimination

The Recursive Feature Elimination (RFE) method is a feature selection approach. It works by recursively removing attributes and building a model on those attributes that remain. It uses the model accuracy to identify which attributes (and combination of attributes) contribute the most to predicting the target attribute.

In [1]:
# Recursive Feature Elimination
from sklearn import datasets
from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression
# load the iris datasets
dataset = datasets.load_iris()
# create a base classifier used to evaluate a subset of attributes
model = LogisticRegression()
# create the RFE model and select 3 attributes
rfe = RFE(model, 3)
rfe = rfe.fit(dataset.data, dataset.target)
# summarize the selection of the attributes
print(rfe.support_)
print(rfe.ranking_)
[False  True  True  True]
[2 1 1 1]

Feature Scaling :

Sometimes, you may notice that certain attributes have a higher ‘magnitude’ than others. An example might be a person’s income – as compared to his age. In such cases, for certain models (such as Ridge Regression), it is infact necessary that you scale all your attributes to comparable/equivalent ranges. This prevents your model from giving greater weightage to certain attributes as compared to others.

Feature Scaling in Python

In [6]:
# An example
# Importing pandas
import pandas as pd
# Importing training data set
X_train=pd.read_csv(r'C:\Users\piush\Desktop\Dataset\loan_prediction-1\X_train.csv')
Y_train=pd.read_csv(r'C:\Users\piush\Desktop\Dataset\loan_prediction-1\Y_train.csv')
# Importing testing data set
X_test=pd.read_csv(r'C:\Users\piush\Desktop\Dataset\loan_prediction-1\X_test.csv')
Y_test=pd.read_csv(r'C:\Users\piush\Desktop\Dataset\loan_prediction-1\Y_test.csv')
In [7]:
import matplotlib.pyplot as plt
%matplotlib inline
X_train[X_train.dtypes[(X_train.dtypes=="float64")|(X_train.dtypes=="int64")]
                        .index.values].hist(figsize=[11,11])
Out[7]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000021C2F6E1FD0>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x0000021C2F77BD30>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000021C2FAB85C0>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x0000021C2FB2A128>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000021C2FB73160>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x0000021C2FBB1240>]], dtype=object)
In [9]:
# Importing MinMaxScaler and initializing it
from sklearn.preprocessing import MinMaxScaler
min_max=MinMaxScaler()
# Scaling down both train and test data set
X_train_minmax=min_max.fit_transform(X_train[['ApplicantIncome', 'CoapplicantIncome',
                'LoanAmount', 'Loan_Amount_Term', 'Credit_History']])
X_test_minmax=min_max.fit_transform(X_test[['ApplicantIncome', 'CoapplicantIncome',
                'LoanAmount', 'Loan_Amount_Term', 'Credit_History']])
In [15]:
### Fitting k-NN on our scaled data set
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
knn=KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train_minmax,Y_train)
# Checking the model's accuracy
accuracy_score(Y_test,knn.predict(X_test_minmax))
C:\Anaconda3\lib\site-packages\ipykernel\__main__.py:5: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
Out[15]:
0.75

Feature Standardization

In the previous section, we fitted a kNN learner on the data set. After scaling down the data, we have got an accuracy of 75% .However on Logistic Regression accuracy after scaling is 63%.

Many machine learning algorithms in sklearn requires standardized data which means having zero mean and unit variance.

Standardization (or Z-score normalization) is the process where the features are rescaled so that they’ll have the properties of a standard normal distribution with μ=0 and σ=1, where μ is the mean (average) and σ is the standard deviation from the mean.

Elements such as l1 ,l2 regularizer in linear models (logistic comes under this category) and RBF kernel in SVM in objective function of learners assumes that all the features are centered around zero and have variance in the same order.

Features having larger order of variance would dominate on the objective function.

In [16]:
# Standardizing the train and test data
from sklearn.preprocessing import scale
X_train_scale=scale(X_train[['ApplicantIncome', 'CoapplicantIncome',
                'LoanAmount', 'Loan_Amount_Term', 'Credit_History']])
X_test_scale=scale(X_test[['ApplicantIncome', 'CoapplicantIncome',
               'LoanAmount', 'Loan_Amount_Term', 'Credit_History']])
# Fitting logistic regression on our standardized data set
from sklearn.linear_model import LogisticRegression
log=LogisticRegression(penalty='l2',C=.01)
log.fit(X_train_scale,Y_train)
# Checking the model's accuracy
accuracy_score(Y_test,log.predict(X_test_scale))
C:\Anaconda3\lib\site-packages\sklearn\utils\validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
Out[16]:
0.75

Feature Extraction :

Feature extraction involves a host of algorithms that automatically generate a new set of features from your raw attributes. Some observations are far too voluminous in their raw state to be modeled by predictive modeling algorithms directly.

Common examples include image, audio, and textual data, but could just as easily include tabular data with millions of attributes.

Dimensionality reduction falls under this category.

For tabular data, this might include projection methods like Principal Component Analysis and unsupervised clustering methods. For image data, this might include line or edge detection. Depending on the domain, image, video and audio observations lend themselves to many of the same types of DSP methods.

Key to feature extraction is that the methods are automatic (although may need to be designed and constructed from simpler methods) and solve the problem of unmanageably high dimensional data, most typically used for analog observations stored in digital formats.

Feature Construction:

The manual construction of new features from raw data

The best results come down to you, the practitioner, crafting the features.

Feature importance and selection can inform you about the objective utility of features, but those features have to come from somewhere.

You need to manually create them. This requires spending a lot of time with actual sample data (not aggregates) and thinking about the underlying form of the problem, structures in the data and how best to expose them to predictive modeling algorithms.

With tabular data, it often means a mixture of aggregating or combining features to create new features, and decomposing or splitting features to create new features.

With textual data, it often means devising document or context specific indicators relevant to the problem. With image data, it can often mean enormous amounts of time prescribing automatic filters to pick out relevant structures.

Feature Learning:

The automatic identification and use of features in raw data

Representation learning or feature learning is an effort towards this goal.

Modern deep learning methods are achieving some success in this area, such as autoencoders and restricted Boltzmann machines. They have been shown to automatically and in a unsupervised or semi-supervised way, learn abstract representations of features (a compressed form), that in turn have supported state-of-the-art results in domains such as speech recognition, image classification, object recognition and other areas.

We do not have automatic feature extraction or construction, yet, and we will probably never have automatic feature engineering.

Pipelines, GridSearchCV and FeatureUnions in scikit-learn

The pipeline module of scikit-learn allows you to chain transformers and estimators together in such a way that you can use them as a single unit. This comes in very handy when you need to jump through a few hoops of data extraction, transformation, normalization, and finally train your model (or use it to generate predictions) scikitlearn.

Pipelines help you prevent data leakage in your test harness by ensuring that data preparation like standardization is constrained to each fold of your cross validation procedure.

The pipeline is defined with two steps:

  1. Standardize the data.
  2. Learn a Linear Discriminant Analysis model.
In [26]:
# Create a pipeline that standardizes the data then creates a model
from pandas import read_csv
from sklearn.cross_validation import KFold
from sklearn.cross_validation import cross_val_score
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
# load data
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data"
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
dataframe = read_csv(url, names=names)
array = dataframe.values
X = array[:,0:8]
Y = array[:,8]
# create pipeline
estimators = []
estimators.append(('standardize', StandardScaler()))
estimators.append(('lda', LinearDiscriminantAnalysis()))
model = Pipeline(estimators)
# evaluate pipeline
num_folds = 10
num_instances = len(X)
seed = 7
kfold = KFold(n=num_instances, n_folds=num_folds, random_state=seed)
results = cross_val_score(model, X, Y, cv=kfold)
print(results.mean())
0.773462064252

The example below demonstrates the pipeline defined with four steps:

  1. Feature Extraction with Principal Component Analysis (3 features)
  2. Feature Extraction with Statistical Selection (6 features)
  3. Feature Union
  4. Learn a Logistic Regression Model

The pipeline is then evaluated using 10-fold cross validation.

In [27]:
# Create a pipeline that extracts features from the data then creates a model
from pandas import read_csv
from sklearn.cross_validation import KFold
from sklearn.cross_validation import cross_val_score
from sklearn.pipeline import Pipeline
from sklearn.pipeline import FeatureUnion
from sklearn.linear_model import LogisticRegression
from sklearn.decomposition import PCA
from sklearn.feature_selection import SelectKBest
# load data
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data"
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
dataframe = read_csv(url, names=names)
array = dataframe.values
X = array[:,0:8]
Y = array[:,8]
# create feature union
features = []
features.append(('pca', PCA(n_components=3)))
features.append(('select_best', SelectKBest(k=6)))
feature_union = FeatureUnion(features)
# create pipeline
estimators = []
estimators.append(('feature_union', feature_union))
estimators.append(('logistic', LogisticRegression()))
model = Pipeline(estimators)
# evaluate pipeline
num_folds = 10
num_instances = len(X)
seed = 7
kfold = KFold(n=num_instances, n_folds=num_folds, random_state=seed)
results = cross_val_score(model, X, Y, cv=kfold)
print(results.mean())
0.776042378674

2 thoughts on “The Comprehensive Guide for Feature Engineering

  1. Great post. Learnt a lot! Can you point me to some kaggle (or other) notebook, which has most of these techniques applied to a single data set?

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