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Machine Learning Basics

27

Jan'17

203.5.3 Practice : Non Linear Decision Boundary

In previous section, we studied about Decision Boundary – Logistic Regression Linear decision boundaries is not always way to go, …

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27

Jan'17

203.5.1 Neural Networks : A Recap of Logistic Regression

In previous section, we studied about Cross Validation In this post we will just revise our understanding of how logistic …

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27

Jan'17

203.3.10 Pruning a Decision Tree in R

Pruning In previous section, we studied about The Problem of Over fitting the Decision Tree Growing the tree beyond a …

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27

Jan'17

203.3.5 Information Gain in Decision Tree Split

Information Gain In previous section, we studied about How to Calculate Entropy for Decision Tree Split? Information Gain= entropyBeforeSplit – …

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27

Jan'17

203.3.4 How to Calculate Entropy for Decision Tree Split?

LAB: Entropy Calculation – Example In previous section, we studied about  How Decision tree Splits works? Calculate entropy at the …

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27

Jan'17

203.3.3 How Decision tree Splits works?

The Splitting Criterion In previous section, we studied about The Decision Tree Approach The best split is The split that …

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