Which technique can help reduce overfitting in machine learning models? Increasing model complexity Decreasing the amount of training data Regularization Ignoring feature importance

The technique that can help reduce overfitting in machine learning models is Regularization.

Here's a step-by-step explanation:

1. Overfitting occurs when a machine learning model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. This means that the noise or random fluctuations in the training data is picked up and learned as concepts by the model.

2. Regularization is a technique used to prevent overfitting by adding a penalty term to the loss function. The penalty term controls the complexity of the model, making it harder for the model to overfit the training data.

3. There are different types of regularization techniques like L1 and L2 regularization. L1 regularization adds an L1 penalty equal to the absolute value of the magnitude of coefficients. L2 regularization adds an L2 penalty equal to the square of the magnitude of coefficients.

4. Regularization works by introducing additional information (bias) to penalize extreme parameter weights. It does not necessarily reduce overfitting, but it does limit the complexity of the model to reduce the risk of overfitting.

5. Increasing model complexity and decreasing the amount of training data can actually lead to overfitting. Ignoring feature importance can lead to a model that doesn't perform well because it's not learning the most predictive features.

So, among the options provided, Regularization is the technique that can help reduce overfitting in machine learning models.