Arrange the following steps in the correct order for applying regularization in predictive modeling: A) Analyze the model's performance on training data, B) Choose a regularization technique, C) Evaluate the model on validation data, D) Train the model with regularization applied.

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Analyze the model's performance on training data

Choose a regularization technique

Evaluate the model on validation data

Train the model with regularization applied

Understanding the Answer

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Answer

First, pick the regularization method that fits the problem, such as L1 or L2 (B). Next, train the model using that technique, so the weights are penalized during learning (D). After training, look at how the model behaves on the training data to see if it is over‑fitting or under‑fitting (A). Finally, test the trained model on a separate validation set to confirm its generalization ability (C). This sequence ensures the regularizer is applied correctly and its effect is evaluated.

Detailed Explanation

First you pick a regularization technique, like deciding on a rule to keep the model simple. Other options are incorrect because Starting with training data analysis assumes you already know how to keep the model simple; Evaluating on validation data before training is impossible because the model has not learned anything yet.

Key Concepts

Regularization in predictive models

Model evaluation

Training techniques

Topic

Regularizers in Predictive Models

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easy level question

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understand

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