203.3.9 The Problem of Over fitting the Decision Tree
LAB: The Problem of Over fitting In previous section, we studied about Validating the Tree Import Dataset: “Buyers Profiles/Train_data.csv” …
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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 …
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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 …
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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 …
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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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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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