Effortlessly Splitting Data with sklearn train_test_split

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Split Your Dataset With scikit-learn’s train_test_split()

One of the key aspects of supervised machine learning is model evaluation and validation. When you evaluate the predictive performance of your model, it’s essential that the process be unbiased. Using train_test_split() from the data science library scikit-learn, you can split your dataset into subsets that minimize the potential for bias in your evaluation and validation process.

In this tutorial, you’ll learn:

• Why you need to split your dataset in supervised machine learning
• Which subsets of the dataset you need for an unbiased evaluation of your model
• How to use train_test_split() to split your data
• How to combine train_test_split() with prediction methods

In addition, you’ll get information on related tools from sklearn.model_selection.

The Importance of Data Splitting

Supervised machine learning is about creating models that precisely map the given inputs (independent variables, or predictors) to the given outputs (dependent variables, or responses).

How you measure the precision of your model depends on the type of problem you’re trying to solve. In regression analysis, you typically use the coefficient of determination, root-mean-square error, mean absolute error, or similar quantities. For classification problems, you often apply accuracy, precision, recall, F1 score, and related indicators.

The acceptable numeric values that measure precision vary from field to field. You can find detailed explanations from Statistics By Jim, Quora, and many other sources.

Training, Validation, and Test Sets

To ensure an unbiased evaluation of your model, it’s crucial to split your dataset into different subsets. The three most common subsets used are the training set, the validation set, and the test set.

The training set is used to train the model. You use this subset to tune the model’s hyperparameters and adjust the weight and biases of the model’s features.

The validation set is used to evaluate the performance of the model during training. You use this subset to fine-tune the model and test different configurations or architectures.

The test set is the final subset that you use to assess the model’s performance. This subset is independent of the training and validation sets and is used to measure the model’s ability to generalize to new, unseen data.

By splitting the dataset into these subsets, you can avoid overfitting or underfitting the model. Overfitting occurs when the model performs well on the training data but fails to generalize to new data. Underfitting occurs when the model fails to capture the pattern in the training data and performs poorly on both the training and test data.

Prerequisites for Using train_test_split()

Before using the train_test_split() function from scikit-learn, you need to ensure that you have the necessary prerequisites installed.

First, you need to have Python installed on your local machine. You can download Python from the official website and follow the installation instructions.

Once you have Python installed, you need to install the scikit-learn library. You can easily do this using pip, the package installer for Python. Open your command prompt or terminal and run the following command:

pip install -U scikit-learn

After installing scikit-learn, you can import the train_test_split() function from the sklearn.model_selection module in your Python script or Jupyter notebook.

Application of train_test_split()

Now that you have the necessary prerequisites installed, you can proceed to split your dataset using train_test_split().

The train_test_split() function takes several parameters. The most important parameters are X, which represents the features (independent variables), and y, which represents the target variable (dependent variable).

Here is an example of how to split your dataset using train_test_split():

from sklearn.model_selection import train_test_split

X = dataset.drop("target", axis=1)
y = dataset["target"]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

In the above code snippet, we first import the train_test_split() function from the sklearn.model_selection module. We then define our features X and target variable y based on our dataset. Finally, we use train_test_split() to split our data into training and test sets, with 80% of the data used for training (X_train and y_train) and 20% of the data used for testing (X_test and y_test).

By specifying the test_size parameter, we can control the proportion of the data that is used for testing. In this case, we set test_size=0.2, which means that 20% of the data will be used for testing, while 80% will be used for training.

The random_state parameter ensures reproducibility of the results. By setting it to a specific value (e.g., random_state=42), we can obtain the same split every time we run the code. This is useful for consistent evaluation and comparison of different models.

Supervised Machine Learning With train_test_split()

Once you have split your dataset using train_test_split(), you can apply supervised machine learning algorithms to train and evaluate your model.

Let’s look at some examples of how you can use train_test_split() for linear regression, regression, and classification problems.

Minimalist Example of Linear Regression

from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

model = LinearRegression()
model.fit(X_train, y_train)

y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)

print(f"Mean Squared Error: {mse}")

In this example, we import the LinearRegression class from sklearn.linear_model and the mean_squared_error function from sklearn.metrics. We then create an instance of the LinearRegression class and fit the model to the training data using the fit() method. We predict the target variable y for the test data using the predict() method and calculate the mean squared error between the predicted and actual values.

Regression Example

from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error

model = RandomForestRegressor()
model.fit(X_train, y_train)

y_pred = model.predict(X_test)
mae = mean_absolute_error(y_test, y_pred)

print(f"Mean Absolute Error: {mae}")

In this example, we import the RandomForestRegressor class from sklearn.ensemble and the mean_absolute_error function from sklearn.metrics. We create an instance of the RandomForestRegressor class and fit the model to the training data. We then predict the target variable y for the test data and calculate the mean absolute error between the predicted and actual values.

Classification Example

from sklearn.svm import SVC
from sklearn.metrics import accuracy_score

model = SVC()
model.fit(X_train, y_train)

y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)

print(f"Accuracy: {accuracy}")

In this example, we import the SVC class from sklearn.svm and the accuracy_score function from sklearn.metrics. We create an instance of the SVC class and fit the model to the training data. We then predict the target variable y for the test data and calculate the accuracy of the model by comparing the predicted values with the actual values.

Other Validation Functionalities

Besides splitting your dataset, scikit-learn provides other functionalities for model evaluation and validation. These include cross-validation, grid search, and performance metrics for regression and classification problems.

Cross-validation allows you to evaluate the performance of your model using different subsets of the data. Grid search helps you find the best hyperparameters for your model by systematically searching through a parameter grid. Performance metrics provide various measures of model performance, such as accuracy, precision, recall, and F1 score.

You can explore these functionalities in more detail in the scikit-learn documentation and use them to enhance your model evaluation and validation process.

Conclusion

In this tutorial, you learned about the importance of splitting your dataset in supervised machine learning and how to use train_test_split() from scikit-learn to split your data into training and test sets. You also saw examples of how to apply train_test_split() in conjunction with linear regression, regression, and classification algorithms.

Splitting your dataset is crucial for obtaining unbiased evaluation and validation of your model. It helps prevent overfitting or underfitting and ensures that your model can effectively generalize to new, unseen data. By combining train_test_split() with other validation functionalities provided by scikit-learn, you can further enhance your model evaluation and improve your machine learning results.