Programming for Data Science, Lec 12: Machine Learning in Python using scikit-learn (sklearn)
#machinelearning #datascience #scikitlearn
How to use sklearn?
The workflow for model fitting and prediction using scikit-learn (sklearn) for supervised learning tasks is standardized and straightforward, making it easy to apply across various models and datasets.
1. Choose a Model
Select a suitable model from scikit-learn based on the type of supervised learning problem at hand (regression or classification). For example, `LinearRegression` for a regression problem, or `LogisticRegression` for a binary classification problem.
2. Import the Model
Import the chosen model from the appropriate module within scikit-learn. For example:
```
from sklearn.linear_model import LinearRegression
```
3. Prepare the Data
Your dataset should be divided into features (independent variables) and the target (dependent variable). The features are usually represented as a 2D array (or DataFrame) `X`, and the target as a 1D array `y`.
4. Split the Data
Use `train_test_split` from `sklearn.model_selection` to split your data into training and testing sets. This step is crucial for evaluating the model's performance on unseen data.
```
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
```
5. Initialize the Model
Instantiate the model with any desired parameters. For example:
```
model = LinearRegression()
```
6. Fit the Model
Train the model on your training data by calling the `fit` method.
```
model.fit(X_train, y_train)
```
7. Make Predictions
Once the model is trained, use it to make predictions. For regression tasks, use the `predict` method. For classification, `predict` gives you the predicted class labels, while `predict_proba` gives you the probabilities for each class.
```
predictions = model.predict(X_test)
```
8. Evaluate the Model
Assess the model's performance using appropriate metrics. For regression, common metrics include Mean Absolute Error (MAE), Mean Squared Error (MSE), or R-squared. For classification, you might use accuracy, precision, recall, F1 score, or AUC-ROC.
```
from sklearn.metrics import mean_squared_error
mse = mean_squared_error(y_test, predictions)
```
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