We will compare several regression methods by using the same dataset. We will try to predict the price of a house as a function of its attributes.

```
import numpy as np
import matplotlib.pyplot as plt
%pylab inline
```

## Import the Boston House Pricing Dataset

```
from sklearn.datasets import load_boston
boston = load_boston()
print (boston.data.shape)
print (boston.feature_names)
print (np.max(boston.target), np.min(boston.target), np.mean(boston.target))
```

```
print (boston.data[0])
print (np.max(boston.data), np.min(boston.data), np.mean(boston.data))
```

You should try printing boston.DESCR to get a feel of what each feature means. This is a very healthy habit: machine learning is not just number crunching, understanding the problem we are facing is crucial, especially to select the best learning model to use.

```
print (boston.DESCR)
```

###### we start slicing our learning set into training and testing datasets, and normalizing the data

###### Separate train and test

```
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target, test_size=0.25, random_state=33)
```

###### Find the most important features

```
from sklearn.feature_selection import *
fs=SelectKBest(score_func=f_regression,k=5)
X_new=fs.fit_transform(X_train,y_train)
print (zip(fs.get_support(),boston.feature_names))
x_min, x_max = X_new[:,0].min() - .5, X_new[:, 0].max() + .5
y_min, y_max = y_train.min() - .5, y_train.max() + .5
#fig=plt.figure()
#fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)
# Two subplots, unpack the axes array immediately
fig, axes = plt.subplots(1,5)
fig.set_size_inches(12,12)
for i in range(5):
axes[i].set_aspect('equal')
axes[i].set_title('Feature ' + str(i))
axes[i].set_xlabel('Feature')
axes[i].set_ylabel('Median house value')
axes[i].set_xlim(x_min, x_max)
axes[i].set_ylim(y_min, y_max)
sca(axes[i])
plt.scatter(X_new[:,i],y_train)
```

### Normalize data

```
from sklearn.preprocessing import StandardScaler
scalerX = StandardScaler().fit(X_train)
scalery = StandardScaler().fit(y_train)
X_train = scalerX.transform(X_train)
y_train = scalery.transform(y_train)
X_test = scalerX.transform(X_test)
y_test = scalery.transform(y_test)
print (np.max(X_train), np.min(X_train), np.mean(X_train), np.max(y_train), np.min(y_train), np.mean(y_train))
```

Accuracy is not a good idea in regression as metrics, since we are predicting real values, it is almost impossible for us to predict exactly the final value.

There are several measures that can be used (you can look at the list of functions under sklearn.metrics module). The most common is the R2 score, or coefficient of determination that measures the proportion of the outcomes variation explained by the model, and is the default score function for regression methods in scikit-learn.

This score reaches its maximum value of 1 when the model perfectly predicts all the test target values.

Using this measure, we will build a function that trains a model and evaluates its performance using five-fold cross-validation and the coefficient of determination

```
from sklearn.cross_validation import *
def train_and_evaluate(clf, X_train, y_train):
clf.fit(X_train, y_train)
print ("Coefficient of determination on training set:",clf.score(X_train, y_train))
# create a k-fold croos validation iterator of k=5 folds
cv = KFold(X_train.shape[0], 5, shuffle=True, random_state=33)
scores = cross_val_score(clf, X_train, y_train, cv=cv)
print ("Average coefficient of determination using 5-fold crossvalidation:",np.mean(scores))
```

### a linear model

The question that linear models try to answer is which hyperplane in the 14-dimensional space created by our learning features (including the target value) is located closer to them. After this hyperplane is found, prediction reduces to calculate the projection on the hyperplane of the new point, and returning the target value coordinate.

The usual measure is least squares: calculate the distance of each instance to the hyperplane, square it (to avoid sign problems), and sum them. The hyperplane whose sum is smaller is the least squares estimator (the hyperplane in the case if two dimensions are just a line).

Since we don’t know how our data fits (it is difficult to print a 14-dimension scatter plot!), we will start with a linear model called SGDRegressor. SGDRegressor tries to find the hyperplane that minimizes a certain loss function (typically, the sum of squared distances from each instance to the hyperplane). It uses Stochastic Gradient Descent to find the minimum.

```
from sklearn import linear_model
clf_sgd = linear_model.SGDRegressor(loss='squared_loss', penalty=None, random_state=42)
train_and_evaluate(clf_sgd,X_train,y_train)
```

We can print the hyperplane coefficients our method has calculated, which is as follows

```
print (clf_sgd.coef_)
```

You probably noted the penalty=None parameter when we called the method. The penalization parameter for linear regression methods is introduced to avoid overfitting. It does this by penalizing those hyperplanes having some of their coefficients too large, seeking hyperplanes where each feature contributes more or less the same to the predicted value. This parameter is generally the L2 norm (the squared sums of the coefficients) or the L1 norm (that is the sum of the absolute value of the coefficients). Let’s see how our model works if we introduce an L2 penalty.

```
clf_sgd1 = linear_model.SGDRegressor(loss='squared_loss', penalty='l2', random_state=42)
train_and_evaluate(clf_sgd1,X_train,y_train)
```

```
clf_sgd2 = linear_model.SGDRegressor(loss='squared_loss', penalty='l1', random_state=42)
train_and_evaluate(clf_sgd2,X_train,y_train)
```

```
clf_sgd3 = linear_model.SGDRegressor(loss='squared_loss', penalty='elasticnet', random_state=42)
train_and_evaluate(clf_sgd3,X_train,y_train)
```

#### Ridge regression

```
clf_ridge = linear_model.Ridge()
train_and_evaluate(clf_ridge,X_train,y_train)
```

In this case, we did not obtain an improvement

#### Support Vector Machines for regression

The regression version of SVM can be used instead to find the hyperplane.

```
from sklearn import svm
clf_svr= svm.SVR(kernel='linear')
train_and_evaluate(clf_svr,X_train,y_train)
```

Here, we had no improvement. However, one of the main advantages of SVM is that (using what we called the kernel trick) we can use a nonlinear function, for example, a polynomial function to approximate our data.

```
clf_svr_poly= svm.SVR(kernel='poly')
train_and_evaluate(clf_svr_poly,X_train,y_train)
```

Now, our results are six points better in terms of coefficient of determination. We can actually improve this by using a Radial Basis Function (RBF) kernel.

```
clf_svr_rbf= svm.SVR(kernel='rbf')
train_and_evaluate(clf_svr_rbf,X_train,y_train)
```

RBF kernels have been used in several problems and have shown to be very effective. Actually, RBF is the default kernel used by SVM methods in scikit-learn.

### Random Forests

When used for regression, the tree growing procedure is exactly the same, but at prediction time, when we arrive at a leaf, instead of reporting the majority class, we return a representative real value, for example, the average of the target values.

Actually, we will use Extra Trees, implemented in the ExtraTreesRegressor class within the sklearn.ensemble module. This method adds an extra level of randomization. It not only selects for each tree a different, random subset of features, but also randomly selects the threshold for each decision.

```
from sklearn import ensemble
clf_et=ensemble.ExtraTreesRegressor(n_estimators=10,random_state=42)
train_and_evaluate(clf_et,X_train,y_train)
```

The first thing to note is that we have not only completely eliminated underfitting (achieving perfect prediction on training values), but also improved the performance by three points while using cross-validation. An interesting feature of Extra Trees is that they allow computing the importance of each feature for the regression task.

```
importances = clf_et.feature_importances_
std = np.std([tree.feature_importances_ for tree in clf_et.estimators_],
axis=0)
indices = np.argsort(importances)[::-1]
# Print the feature ranking
print("Feature ranking:")
for f in range(X_train.shape[1]):
print("%d. feature %d (%f)" % (f + 1, indices[f], importances[indices[f]]))
# Plot the feature importances of the forest
plt.figure()
plt.title("Feature importances")
plt.bar(range(X_train.shape[1]), importances[indices],
color="r", yerr=std[indices], align="center")
plt.xticks(range(X_train.shape[1]), indices)
plt.xlim([-1, X_train.shape[1]])
plt.show()
```

```
print (list(zip(clf_et.feature_importances_,boston.feature_names)))
```

### Evaluation

```
from sklearn import metrics
def measure_performance(X,y,clf, show_accuracy=True, show_classification_report=True, show_confusion_matrix=True, show_r2_score=False):
y_pred=clf.predict(X)
if show_accuracy:
print ("Accuracy:{0:.3f}".format(metrics.accuracy_score(y,y_pred)),"\n")
if show_classification_report:
print ("Classification report")
print (metrics.classification_report(y,y_pred),"\n")
if show_confusion_matrix:
print ("Confusion matrix")
print (metrics.confusion_matrix(y,y_pred),"\n")
if show_r2_score:
print ("Coefficient of determination:{0:.3f}".format(metrics.r2_score(y,y_pred)),"\n")
measure_performance(X_test,y_test,clf_et, show_accuracy=False, show_classification_report=False,show_confusion_matrix=False, show_r2_score=True)
```