Classification Week 1 - Linear classifier and logistic regression

gistfile1.txt

Purpose
Data -> classifier -> intelligence

Input x -> sentence sentiment classifier -> y (positive or negative)

App:
Spam filtering
image classification

Impact of classification
Course overview
models: linear classifier, logistic regression, decision trees, ensembles
algorithms: gradient, stochastic gradient, recursive greedy, boosting
core ML concept: alleviating overfitting, missing data, precision recall, online learning

Linear Classifiers: use training data to learn a weight or coefficient for each word
Intuition 
Scoring a sentence
output is weighted sum of input
Decision Boundary
Class probability
link function: suqeeze real line into 0..1 => generalize model

Logistic regression
sigmoid function => sigmoid score = 1/(1+exp(-score))
find best classifier = maximize quality metric over all possible w0 w1 w2

Encoding categorical inputs
Multiclass classification
1 versus all => +1 for 1 class and -1 for the rest