AdaBoost The AdaBoost (adaptive boosting) algorithm was proposed in 1995 by Yoav Freund and Robert Shapire as a general method for generating a strong classifier out of a set of weak classifiers . AdaBoost works even when the classifiers come from a continuum of potential classifiers (such as neural networks, linear discriminants, etc.) AdaBoost Pros: Low generalization error, easy to…

# Category: Machine Learning

## Apriori Algorithm (Python 3.0)

Apriori Algorithm The Apriori algorithm principle says that if an itemset is frequent, then all of its subsets are frequent.this means that if {0,1} is frequent, then {0} and {1} have to be frequent. The rule turned around says that if an itemset is infrequent, then its supersets are also infrequent. We first need to find the frequent itemsets, and…

## Principal Component Analysis in scikit-learn

Principal Component Analysis (PCA) is an orthogonal linear transformation that turns a set of possibly correlated variables into a new set of variables that are as uncorrelated as possible. The new variables lie in a new coordinate system such that the greatest variance is obtained by projecting the data in the first coordinate, the second greatest variance by projecting in…

## Naiive Bayes in scikit-learn

Naïve Bayes is a simple but powerful classifier based on a probabilistic model derived from the Bayes’ theorem. Basically it determines the probability that an instance belongs to a class based on each of the feature value probabilities. One of the most successful applications of Naïve Bayes has been within the field of Natural Language Processing (NLP). NLP is a…

## Decision Trees in scikit-learn

Decision trees are very simple yet powerful supervised learning methods, which constructs a decision tree model, which will be used to make predictions. The main advantage of this model is that a human being can easily understand and reproduce the sequence of decisions (especially if the number of attributes is small) taken to predict the target class of a new…

## Support Vector Machine in scikit-learn- part 2

continued from part 1 In [8]: print_faces(faces.images, faces.target, 400) Training a Support Vector Machine Support Vector Classifier (SVC) will be used for classification The SVC implementation has different important parameters; probably the most relevant is kernel, which defines the kernel function to be used in our classifier In [10]: from sklearn.svm import SVC svc_1 = SVC(kernel=’linear’) print (svc_1) SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,…

## Support Vector Machine in scikit-learn – part 1

Support Vector Machines has become one of the state-of-the-art machine learning models for many tasks with excellent results in many practical applications. One of the greatest advantages of Support Vector Machines is that they are very effective when working on high-dimensional spaces, that is, on problems which have a lot of features to learn from. They are also very effective…

## Regression in scikit-learn

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. In [6]: import numpy as np import matplotlib.pyplot as plt %pylab inline Populating the interactive namespace from numpy and matplotlib Import the Boston House Pricing Dataset In [9]: from sklearn.datasets import load_boston boston = load_boston()…