1. Split the dataset into training and testing sets: The first step in evaluating a machine learning model is to split the dataset into training and testing sets. This allows us to assess the model’s performance on unseen data. For example, if we have a dataset of 1000 customer records, we can split it into 800 training records and 200 testing records.

2. Train the model on the training set: Once the dataset is split, we can train the model on the training set. This step is necessary to learn the model’s parameters and to tune the hyperparameters.

3. Evaluate the model on the testing set: After training the model, we can evaluate it on the testing set. This allows us to measure the model’s performance on unseen data. Common metrics used to evaluate machine learning models include accuracy, precision, recall, and F1 score.

4. Make improvements: If the model’s performance is not satisfactory, we can make improvements by tuning the hyperparameters or by using a different model. We can also use cross-validation to further improve the model’s performance.

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