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# Decision Trees

27

Jan'17

### 203.3.8 Practice : Validating the Tree

Tree Validation In previous section, we studied about Building a Decision Tree in R Accuracy=(TP+TN)/(TP+FP+FN+TN) Misclassification Rate=(FP+FN)/(TP+FP+FN+TN) LAB: Tree Validation …

27

Jan'17

### 203.3.7 Building a Decision Tree in R

LAB: Decision Tree Building In previous section, we studied about Information Gain in Decision Tree Split Import Data:Ecom_Cust_Relationship_Management/Ecom_Cust_Survey.csv How many …

27

Jan'17

### 203.3.6 The Decision Tree Algorithm

The Decision tree Algorithm In previous section, we studied about Information Gain in Decision Tree Split The major step is …

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 – …

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 …

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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