Andrew Ng machine learning portal term notes 1-

The term 1

1.1 Generalization

The learned ability to model for new samples called generalization

1.2 over-fitting - the key obstacle

Learner can fit all of the data sample, that some of the training sample its own characteristics as a potential general nature of all samples , resulting in a reduced ability of generalization phenomenon.

Excessive function which assumes the characteristic variable
Can only be alleviated, can not be eliminated
Common cause factors: the ability to learn to have a good

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1.2.1 Solution

Effect of human or rounding algorithm using characteristic variables is not high
Regularization: wherein all the variables but retained the size reduction parameter

1.3 error rate

Misclassification number of samples as a percentage of the total sample, set a small number of samples == category as a positive category ==

1.3.1 precision P (Precision)

Positive results in the machine predictable result of the user really need the proportion of eg: judge people get cancer, people really get cancer rate

1.3.2 recall / recall R (Recall)

The proportion of share of results of results users really need the machine successfully predict eg: all people get cancer, and can determine the ratio of people get cancer

And P mutually exclusive

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1.3.3 weigh the precision and recall

By changing the determination threshold value, a high threshold, a low P high R; low high low threshold P R

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1.3.4 BEP

Value equal precision and recall

1.3.5 using the harmonic mean is determined based on precision and recall bad algorithm

Each statistical variables reciprocal reciprocal arithmetic average. Higher value to a lower weight
\ [\ frac {1} { F1} = \ frac {1} {2} (\ frac {1} {P} + \ frac {1} {R}) \ tag { 1.1} \]

\[ F_1=2\frac{PR}{P+R}\tag{1.2} \]

Results constant is smaller than the arithmetic mean
\ (F_1 \ in [0, 1] \) , the higher the better

1.4 chi-square distribution

The square of the standard normal distribution

1.5 Boolean data

I.e., logical data type value of 0 or 1