8. Fine-Tuning Classification Algorithms

Activity 8.01: Implementing Different Classification Algorithms

Solution:

1. Import the logistic regression library:

   from sklearn.linear_model import LogisticRegression

2. Fit the model:

   clf_logistic = LogisticRegression(random_state=0,solver='lbfgs')\

                  .fit(X_train[top7_features], y_train)

   clf_logistic

   The preceding code will give the following output:

   LogisticRegression(random_state=0)

3. Score the model:

   clf_logistic.score(X_test[top7_features], y_test)

   You will get the following output: 0.7454031117397454.

   This shows that the logistic regression model is getting an accuracy of 74.5%, which is a mediocre accuracy but serves as a good estimate of the minimum accuracy you can expect.

4. Import the svm library:

   from sklearn import svm

5. Scale the training and testing data as follows:

   from sklearn.preprocessing import MinMaxScaler

   scaling = MinMaxScaler...