When a model works well on training data but performs badly on test data this is known as?

Your model is underfitting the training data when the model performs poorly on the training data. This is because the model is unable to capture the relationship between the input examples (often called X) and the target values (often called Y).

What happens when you train and validate your model on the same data Why is this bad?

The problem of training and testing on the same dataset is that you won’t realize that your model is overfitting, because the performance of your model on the test set is good. The purpose of testing on data that has not been seen during training is to allow you to properly evaluate whether overfitting is happening.

What is the problem if a predictive model is trained and evaluated on the same dataset?

Tackling Overfitting A split of data 66\%/34\% for training to test datasets is a good start. Using cross validation is better, and using multiple runs of cross validation is better again. You want to spend the time and get the best estimate of the models accurate on unseen data.

How do you decide whether the trained model is overfitting or Underfitting?

1. Overfitting is when the model’s error on the training set (i.e. during training) is very low but then, the model’s error on the test set (i.e. unseen samples) is large!
2. Underfitting is when the model’s error on both the training and test sets (i.e. during training and testing) is very high.

What is training error in machine learning?

Training error is the prediction error we get applying the model to the same data from which we trained. Train error is often lower than test error as the model has already seen the training set. It’s then going to fit the training set with lower error than it was going to occur on the test set.

What happens to SVM when the value of c approaches positive infinity?

Higher values of C lead to Overfitting resulting in a low bias and high variance. As the value of C approaches positive infinity there is no room for error as the penalty for misclassification is enormous and it results in heavy overfitting as the model pushes for the best possible accuracy.