Cross-validation is used to test how effective our machine learning model performs. Our Model's performance is dependent on the way we split the data.
Cross-validation is a technique to evaluate model's performance by spliting the data in different ways.
Cross validation is a vital step in evaluating a model. It maximizes the amount of data that is used to train the model as during the course of training, the model is not only trained but it is also tested on all the available data.
5 - Fold Cross-Validation
As you are seeing the above diagram as we split our dataset into five folds we call this process 5- Fold Cross-Validation.
Cross-Validation and Model Performance
- 5 Folds = 5-Fold CV
- 10 Folds = 10-Fold CV
- k Folds = k-Fold CV
- More Folds = More Computationally Expensive
from sklearn.model_selection import cross_val_score from sklearn.linear_model import LinearRegression() model = LinearRegression() cv_result = cross_val_score(model,X,y,cv=5) print(cv_result)
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