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Let us now train the Softmax Regression (Logistic Regression - multi_class-multinomial). We will be doing the following as part of this exercise:

- We will be first training the Softmax Regression (Logistic Regression - multi_class-multinomial) on the training dataset
- Using the trained model, make the prediction on a sample instance and compare the prediction with the actual value.
- Using the trained model, make the prediction on the whole training dataset
- Calculate - accuracy, precision, recall and F1 Score for Softmax Regression (Logistic Regression - multi_class-multinomial).

Please follow the below steps:

Import LogisticRegression from SKLearn

```
from <<your code comes here>> import LogisticRegression
```

Create an instance of LogisticRegression by passing parameters - multi_class="multinomial", solver="lbfgs", C=10 and random_state=42 to the constructor and store this created instance in a variable called 'log_clf'.

```
# using Softmax Regression (multi-class classification problem)
log_clf = LogisticRegression(<<your code comes here>>)
# 'C' is hyprparameter for regularizing L2
# 'lbfgs' is Byoden-Fletcher-Goldfarb-Shanno(BFGS) algorithm
```

Now, train the model on 'scaled' training dataset

```
log_clf.<<your code comes here>>(X_train_scaled, <<your code comes here>>)
```

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

```
y_train_predict = log_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.

```
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 'log_clf' and save the result in variable 'y_train_predict'

```
y_train_predict = log_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 - log_accuracy, log_precision, log_recall and log_f1_score.

```
log_accuracy = <<your code comes here>>(y_train, <<your code comes here>>)
log_precision = <<your code comes here>>(y_train, <<your code comes here>>, average='weighted')
log_recall = <<your code comes here>>(y_train, <<your code comes here>>, average='weighted')
log_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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