A model can be seen as a combination of :

• Hyperparameters

• Algorithm

• Training Data

• Testing Data

To validate a model, you use the :

• Correcting set

• Running set

• Testing set

• Training set

Model Tuning is the process of maximizing a model's performance by changing its ___

Model tuning is :

• a try-and-go process

• a trial-and-error process

• a choose-and-buy process

• a satisfy-and-test process

The Ideal model have :

• Low Variance

• High Variance

• Low Bias

• High Bias

A model that "might always do good prediction, but never great prediction" have :

• High Variance and High Bias

• High Variance and Low Bias

• Low Variance and High Bias

• Low Variance and Low Bias

A model that "might do great sometimes, but it also might do terrible" have :

• High Variance and High Bias

• High Variance and Low Bias

• Low Variance and High Bias

• Low Variance and Low Bias

Bias represents ________ on a model's results due to erroneous assumptions in the machine learning process.

• a systematic error

• an occasional error

Variance, is a type of error that occurs due to a model's _____ to fluctuations in the training set.

• rigidity

• sensitivity

High variance would cause an algorithm to model the noise in the training set. This is most commonly referred to as over____