Be Confident in your Machine Learning Models with the help of Cross-Validation

Cross-validation is a go-to tool to check if your machine-learning model is reliable enough to work on new data. This article will discuss cross-validation, from why it is needed to how to perform it on your data.

Overfitting

Evaluating the trained machine learning model on training data itself is fundamentally wrong. If done, the model will only return the values that it has learned during training. This evaluation will always give 100% accuracy and …

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