Programming for Data Science,Lec 14: Classification in Machine Learning using scikit-learn (sklearn)

#machinelearning #datascience #logisticregression
First, you need to import the `LogisticRegression` class from the `linear_model` module in scikit-learn.
```
from sklearn.linear_model import LogisticRegression
```
Next, you initialize a `LogisticRegression` object. This step allows you to specify various parameters, such as the regularization strength (C), the solver to use for optimization, and the maximum number of iterations the solver performs.
```
model = LogisticRegression()
```
Fitting the model involves adjusting the weights to minimize the cost function using the training data. This is done using the `.fit()` method.
```
model.fit(X_train, y_train)
```
After the model has been fitted, you can use it to make predictions on new data using the `.predict()` method for class labels, or `.predict_proba()` for probabilities of each class.
```
predictions = model.predict(X_test)
probabilities = model.predict_proba(X_test)
```
You can evaluate the performance of your model using various metrics such as accuracy, precision, and recall.
```
from sklearn.metrics import accuracy_score, precision_score, recall_score
accuracy = accuracy_score(y_test, predictions)
precision = precision_score(y_test, predictions)
recall = recall_score(y_test, predictions)
```