1. Machine learning algorithms can be used for classification or regression tasks, with classification predicting a discrete label and regression predicting a real number. 2. K-nearest neighbors (KNN) is a simple supervised learning algorithm that can be used for classification by finding the nearest training example to a new data point and predicting the same label. 3. Naive Bayes classification is another simple algorithm that uses Bayes' theorem to classify text documents based on a "bag of words" representation, making the assumption that words are conditionally independent given a class.

1. Classification
Machine Learning

2. Supervised Learning:
 Classification: Predict a discrete value(label)
associated with feature vector.
 Regression: Predict a real number associated with a
feature vector.
E.g., Use linear regression to fit a curve to data.

3. Example:

4. Distance Matrix:

5. Using Distance Matrix for Classification:
 Simplest approach is probably nearest neighbors.
 Remember training data
 When predicting the label of a new example
 Find the nearest example in the training data
 Predict the label associated with that example.

6. Distance Matrix:

7. Hand-Written Character Recognition:

8. K-nearest neighbors

9. Advantages and Disadvantages of KNN:
Advantages:
 Learning Fast, no explicit training
 No theory Required
 Easy to explain method and results
Disadvantages:
 Memory intensive and predictions can take a long
time.
 No model to shed light on process that generated
data.

10. Naïve Baye’s Text classification:
Why?
 Learn which news articles are of interest.
 Learn to classify web pages category
Basic Intuition:
 Simple (naïve) classification method based on
Bayes rule.
 Relies on very simple representation of documents
 Bag of words

11. Bag of words representation:

12. Naïve Bayes Text Classification:
Bayes Rule:
For a document d and class c
Goal of Classifier:

13. Learn to Classify Text using Naïve Bayes:
Target concept interesting? : Document {+, -}
 Represent each document by vector of words
 One attribute per word position in document
 Learning : Use training examples to estimate
P(+), P(-), P(doc|+), P(doc|-)
Naïve Bayes conditional independence assumption
Where P(ai = Wk|Vj) is probability that a word
in position in i is Wk , given Vj

14. An example: Movie Review
Dictionary: 10 Unique words
< I, loved, the, movie, hated, a, great, good, poor,
acting>

15. Steps:
 Covert the documents into feature sets, where
attributes are possible words, and the values are the
number of times a word occurs in the given
document.
Doc I love
d
the movi
e
hate
d
a great goo
d
poor actin
g
Clas
s
1 1 1 1 1 +
2 1 1 1 1 -
3 2 1 1 1 +
4 1 1 -
5 1 1 1 1 1 +
Let us look at the probabilities per outcomes(+
or -)

16. Naïve Bayes…
 Documents with positive outcomes:
P(+)= 3/5= 0.6
Compute: P(I|+), P(loved|+), P(the|+), P(movie|+), P(a|+),
P(great|+), P(good|+), P(acting|+)
Let n be the number of words in the (+) case: 14, nk the
number of word k occurs in these case(+)
Let P(Wk|+) = (n k + 1)/(n +|vocabulary|)
Doc I loved the movie hate
d
a great goo
d
poo
r
actin
g
Clas
s
1 1 1 1 1 +
3 2 1 1 1 +
5 1 1 1 1 1 +

17. Naïve Bayes…
P(I|+)=0.0833 P(acting|+)=
0.0833
P(loved|+)=0.0833 P(poor|+)=
0.0417
P(the|+)= 0.0833 P(hated|+) =
0.0417
P(movie|+)= 0.2083 P(great|+)=
0.1250
P(a|+)= 0.1250 P(good|+)=
0.1250
 Now, Documents with negative class:
Doc I love
d
the movie hate
d
a gre
at
goo
d
poo
r
acting Clas
s
2 1 1 1 1 -
4 1 1 -

18. P(I|-)= 0.1250 P(acting|-)= 0.1250
P(loved|-)= 0.0625 P(poor|-)= 0.1250
P(the|-)= 0.1250 P(hated|-) = 0.1250
P(movie|-)= 0.1250 P(great|-)= 0.0625
P(a|-)= 0.0625 P(good|-)= 0.0625
Now, Let’s classify a new sentence w.r.t our training
samples:
Test document: I hated the poor acting
If Vj= +;
P(+)*P(I|+)*P(hated|+)*P(the|+)*P(poor|+)*P(acting|+)
6.03× 10^(-7)
If Vj= - ; P(-)*P(I|-)*P(hated|-)*P(the|-)*P(poor|-)*P(acting|-)
1.22 × 10^(-5)