## Naiive Bayes in scikit-learn

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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 […]

## Decision Trees in scikit-learn

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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 […]

## 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 […]

## 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. […]

## 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 […]

## Linear Classification method with ScikitLearn

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This blog is from the book and aimed to be as a learning material for myself only.Linear Classification method implements regularized linear models with stochastic gradient descent (SGD) learning. Each sample estimates the gradient of the loss at a time and the model updates along the way with a decreasing strength schedule (aka learning rate). SGD allows […]