How would I go about completing this exercise? Lesson 2 has been a bit difficult and feeling stuck on this one. Can someone revise it please?

EXERCISE: Initialize the`LogisticRegression`

model with different arguments and try to achieve a higher accuracy. The arguments used for initializing the model are called hyperparameters (to differentiate them from weights and biases - parameters that are learned by the model during training). You can find the full list of arguments here: sklearn.linear_model.LogisticRegression — scikit-learn 0.24.2 documentation

**My attempt:**

from sklearn.linear_model import LogisticRegression

model2 = LogisticRegression(

tol=0.01,

class_weight=‘balanced’,

solver=‘liblinear’)

model2.fit(train_inputs[numeric_cols + encoded_cols], train_targets)

print(model2.intercept_)

X_train2 = train_inputs[numeric_cols + encoded_cols]

X_val2 = val_inputs[numeric_cols + encoded_cols]

X_test2 = test_inputs[numeric_cols + encoded_cols]

train_preds2 = model.predict(X_train2)

train_probs2 = model.predict_proba(X_train2)

–

from sklearn.metrics import accuracy_score

accuracy_score(train_targets, train_preds2)

–

from sklearn.metrics import confusion_matrix

confusion_matrix(train_targets, train_preds2, normalize=‘true’)

–

train_preds = predict_and_plot(X_train2, train_targets, ‘Training’)