Flash Sale: Flat 70% + Addl. 25% Off on all Courses | Use Coupon DS25 in Checkout | Offer Expires In

  Enroll Now

End to End ML Project - Fashion MNIST - Training the Model - DecisionTreeClassifier

Let us now train the DecisionTreeClassifier. We will be doing the following as part of this exercise:

  1. We will be first training the DecisionTreeClassifier on the training dataset
  2. Using the trained model, make the prediction on a sample instance and compare the prediction with the actual value.
  3. Using the trained model, make the prediction on the whole training dataset
  4. Calculate - accuracy, precision, recall and F1 Score for DecisionTreeClassifier.

Please follow the below steps:

Import DecisionTreeClassifier from SKLearn

from <<your code comes here>> import DecisionTreeClassifier

Create an instance of DecisionTreeClassifier by passing parameters - max_depth=50 and random_state=42, and store this created instance in a variable called 'dec_tree_clf'.

dec_tree_clf = DecisionTreeClassifier(<<your code comes here>>)

# Scaling is not needed for Decision Tree algorithm

Now, train the model on training dataset

dec_tree_clf.<<your code comes here>>(X_train, <<your code comes here>>)

Note: Please note that the training might take upto 2-3 minutes of time.

Make prediction on an instance from the training dataset (say instance at index '0' i.e. X_train[0]) using the above trained model 'dec_tree_clf', and store the predicted value in a variable called y_train_predict

y_train_predict = dec_tree_clf.<<your code comes here>>(X_train[0].reshape(1, -1))

Let us compare the actual value to the predicted value of the label. You can use showImage() function to see the image.




Make the predictions on the complete training dataset X_train using the above trained model 'dec_tree_clf' and save the result in variable 'y_train_predict'

y_train_predict = dec_tree_clf.<<your code comes here>>(X_train)

Calculate the various metrics scores like - accuracy, precision, recall, F1 score - using the actual and the predicted values and relevant functions, - and store them in respective variables - dec_tree_accuracy, dec_tree_precision, dec_tree_recall and dec_tree_f1_score.

dec_tree_accuracy = <<your code comes here>>(y_train, <<your code comes here>>)

dec_tree_precision = <<your code comes here>>(y_train, <<your code comes here>>, average='weighted')

dec_tree_recall = <<your code comes here>>(y_train, <<your code comes here>>, average='weighted')

dec_tree_f1_score = <<your code comes here>>(y_train, <<your code comes here>>, average='weighted')

You can print the above metrics values (accuracy, etc.) using the print() function.

No hints are availble for this assesment

Answer is not availble for this assesment

Note - Having trouble with the assessment engine? Follow the steps listed here

Loading comments...