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

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

  1. We will be first training the SGDClassifier 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 SGDClassifier.
INSTRUCTIONS

Please follow the below steps:

Import SGDClassifier from SKLearn

from <<your code comes here>> import SGDClassifier

Create an instance of SGDClassifier, called 'sgd_clf', by passing to it the parameter random_state=42

sgd_clf = SGDClassifier(<<your code comes here>>)

Now, train the model on 'scaled' training dataset

sgd_clf.<<your code comes here>>(X_train_scaled, <<your code comes here>>)

Note: The training might take some time. Approximately 5-10 minutes.

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

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

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

y_train[0] 

y_train_predict[0]

showImage(X_train[0])

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

y_train_predict = sgd_clf.<<your code comes here>>(X_train_scaled)

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 - sgd_accuracy, sgd_precision, sgd_recall and sgq_f1_score.

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

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

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

sgd_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


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