Entropy value

Number of decision trees

Number of neighbors (k-value)

Activation function

Predict continuous values

Find the best decision boundary (hyperplane) to separate classes

Only work with categorical data

Generate random samples

0.80

0.70

0.75

0.67

It cannot handle missing data

It averages multiple decision trees to reduce overfitting

It generates only one decision tree

It is always faster than Decision Trees

120

130

150

100

Principal Component Analysis (PCA)

Logistic Regression

Apriori Algorithm

K-Means Clustering