01. Model Evaluation and Validation assessment

Bias is error due to too much complexity in the learning algorithm you’re using. Bias is often the cause of overfitting.

Bias is error due to erroneous or overly simplistic assumptions in the learning algorithm you’re using. Bias is often the cause of overfitting.

Bias is error due to erroneous or overly simplistic assumptions in the learning algorithm you’re using. Bias is often the cause of underfitting.

Bias is error due to too much complexity in the learning algorithm you’re using. Bias is often the cause of underfitting.

Type I error is a false negative, while Type II error is a false positive.

Type I error is a true positive, while Type II error is a false negative.

Type I error is a false positive, while Type II error is a false negative.

Type I error is a false positive, while Type II error is a true negative.

The data set is divided into k subsets and each time, one of the k subsets is used as the test set and the other k-1 subsets are put together to form a training set. Then the average error across all k trials is computed. This helps prevent underfitting.

The data set is divided into k subsets and each time, one of the k subsets is used as the test set and the other k-1 subsets are put together to form a training set. Then the average error across all k trials is computed. This helps prevent overfitting.