What is Decision Tree Classification?
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Decision Tree Classification is the most powerful and popular tool for doing the classification of a dataset. A decision tree can be learned by splitting the source set into subsets based on the attribute value.
For example, if we want to do the decision tree classification then consider a dataset:
Now, let us perform decision tree classification and our result will look like this:
So, performing the decision tree classification stepwise.
Step 1.Split the dataset having a value less than 60 and greater than 60based upon the information gain or we can say that based upon the entropy.
We are doing the visualization of the decision tree that How the machine will do the classification?
Step 2. Again split the dataset based on information gain or entropy. Split having values less than and greater than 50 but X2 having a value greater than 60.
Step 3. Split based on values having X1 less than 70 and greater than 70 but X2 less than 60.
Step 4. Split points having values less than 20 and greater than 20 but having X1 values greater than 70.
Now, we will do the implementation part. We first import our data set of people who want to buy a specific product.
We should follow the steps to build a Naive Bayes Algorithm.
Step 1. Import the Libraries
Step 2. Importing the Dataset
Step 3. Split the data into a matrix of features(X)(So we are taking ‘Age’ and ‘Salary’ into consideration to do Prediction) and the dependent variable(y).
Step 4. Splitting the matrix of features(X) and dependent variable(y) into training and test set.
Steps 5. Now we do Feature Scaling for ‘Age’ and ‘Salary’ column.
Step 6. Fitting a linear model to test and training dataset.
Step 7. Predicting the Test result.
Step 8. Making the Confusion Matrix to do predictions.
Step 8. Visualization of Dataset.
