What steps will you take to avoid overfitting and Underfitting?

What steps will you take to avoid overfitting and Underfitting?

Specifically, you learned:

1. Underfitting can easily be addressed by increasing the capacity of the network, but overfitting requires the use of specialized techniques.
2. Regularization methods like weight decay provide an easy way to control overfitting for large neural network models.

How can overfitting regression be prevented?

To avoid overfitting a regression model, you should draw a random sample that is large enough to handle all of the terms that you expect to include in your model. This process requires that you investigate similar studies before you collect data.

How hyperparameter and cross validation helps in avoiding Underfitting and overfitting?

You can prevent the model from overfitting by using techniques like K-fold cross-validation and hyperparameter tuning. K-fold cross-validation: In this technique, we generally divide the dataset into three parts. The training part contains 80% data and the test part contains 20% data.

What can we do to deal with overfitting in LightGBM?

According to lightGBM documentation, when facing overfitting you may want to do the following parameter tuning:

1. Use small max_bin.
2. Use small num_leaves.
3. Use min_data_in_leaf and min_sum_hessian_in_leaf.
4. Use bagging by set bagging_fraction and bagging_freq.
5. Use feature sub-sampling by set feature_fraction.

When does hyperparameter optimization lead to overfitting?

And most vexingly, hyperparameter optimization can lead to overfitting: if a researcher runs 400 experiments on the same train-test splits, then performance on the test data is being incorporated into the training data by choice of hyperparameters. This is true even if regularization is being used!

How can you detect an overfitting regression model?

Consequently, you can detect overfitting by determining whether your model fits new data as well as it fits the data used to estimate the model. In statistics, we call this cross-validation, and it often involves partitioning your data.

When to use a hyperparameter in regularization?

Hyperparameter Optimization When introducing a regularization method, you have to decide how much weight you want to give to that regularization method. You can pick larger or smaller values for your complexity penalty depending on how much you think overfitting is going to be a problem for your current use case.

How are hyper parameter tuning techniques used in deep learning?

The approach is based on finding the balance between underfitting and overfitting by examining the training’s test/validation loss for clues of underfitting and overfitting in order to strive for the optimal set of hyper-parameters. The hyper-parameter tuning process is a tightrope walk to achieve a balance between underfitting and overfitting.