Classification 1

Classification examples:

1, multiclass classifier

input : web page

output : what the web page is about, education, finance or technology

2, spam filtering

input : text of email, ip, sender...

output : spam or not spam

3, image classification

input : image pixels

output : predicted object

4, personalized medical diagnosis

input : temperature, x-ray result, lab test result, DNA, life style

output : healthy, flu, cold, pneumonia...

5, reading your mind

input : FMRI image

output : what are you reading, what are you looking at

 

Overview of content

1, Models

linear classifiers

logistic regression : probability

decision trees : provide non linear feature for the data, example : whether a loan is risky or safe

ensembles

2, Algorithms

Gradient, Stochastic gradient, Recursive greedy, Boosting using AdaBoost

3, Core ML

Alleviating overfitting, handling missing data, precision recall, online learning

 

Linear Classifiers

Logistic regression : the most commonly used linear classifiers and one of the most useful ones.

1, Linear classifier : a motivating example

Classifying sentiment of review, build a sentence sentiment classifier, given a sentence, is it a positive sentiment or negative sentiment?

2, Intuition behind linear classifier:

split our data into training set and validation set, then we will feed our traning dataset to some learning algorithm to learn the weights associated with each word, finally we will go back to evaluate its accuracy on that validation set.

3, Decision boudaries

A boundary between positive predictions and negative predictions.

when 2 coefficients are non-zero, it's a line, when 3 coefficients are non-zero, it's a plane, when many coefficients are non-zero, it's a hyperplane.

 Class probabilities

1, Predict class probabilities

In logistic regression, we don't just predict plus one or minus one, we predict a probability, how likely is this review to be positive? how likely is this review to be negative? probabilities give us an indication of how sure we are about predictions we make.

2, Using probabilities in classification

Logistic regression

1, Predicting class probabilities with (generalized) linear models

2, The sigmoid (or logistic) link function

3,