train test split sklearn template in python

To split your dataset into training and testing sets using the scikit-learn library in Python, you can use the train_test_split function from the sklearn.model_selection module. Here's a template on how to use it:

main.py
from sklearn.model_selection import train_test_split

# Assuming you have your features and target data
# X represents the features and y represents the target variable

# Splitting the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

"""
Here, X represents the features and y represents the target variable.
- test_size is the proportion of the dataset to include in the testing set.
  Here, we have set it to 0.2, meaning 20% of the data will be used for testing and 80% for training.
- random_state is an optional parameter that allows you to set a seed for random shuffling of the data.
  This ensures that the split is reproducible.
"""

# You can now use the X_train and y_train for training your model, and X_test and y_test for evaluation or testing.

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By splitting your data into separate training and testing sets, you can train your model on the training set and then evaluate its performance on the unseen testing set. This helps you assess how well your model generalizes to new data.

Remember to import the necessary libraries and preprocess your dataset before using the train_test_split function.

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related categories

sklearn
machine-learning
train-test-split

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