high bias ( under fitting): fixed by trying a larger of features,
high variance (overfitting): fixed by trying a smaller of features, including more training examples.
L1 norm
L2 norm
P values
The p-value is defined as the probability, under the null hypothesis, of obtaining a result equal to or more extreme than what was actually observed.
The smaller the p-value, the higher the significance because it tells the investigator that the hypothesis under consideration may not adequately explain the observation.
Type II error: failure to reject a false null hypothesis
power: it will reject a false null hypothesis