CLASSIFICATION
If
the prediction value tends to be category like yes/no etc then it falls under
classification type.
Examples
1. Check if image
belongs to class ‘face ‘ or ‘cat’
2. Check if an animal is
mammal or reptile
3. Find classes like
good harvest or bad harvest.
4. Identify if a news is
related to politics, sports etc.
5. Identify if an image
is a male/female/child face.
6. Email is spam or not.
7. Identify the patient
is ill or not.
8. Matching Aadhar card
to a template.
9. Checking to see if a
currency inserted in an ATM is fake or real.
CLUSTERING
Grouping
similar data to given number of clusters.
Examples
1. Find all transactions
which are fraudulent in nature.
2. Given a news report,
group similar news together.
3. Movie
recommendations.
4. Product
categorization.
5. Grouping similar
color pixels in an image.
REGRESSION
Regression
is mostly used for predicting values.
1. Find the number of
kgs of rice produced given some climatic conditions and rain data.
2. How tall is a man?
3. How many customers
has viewed daily experience?
4. Given information
about the bungalow, predict the price.
5. Netflix – Given a
user and movie, predict the rating of the movie.
6. Predicting the number
of likes on Facebook post given time, week and usual likes of the person.
7. Predict stock market
price.
8. Predicting the price
of a product based on the market conditions, economy and brand image.
To
find classes or categories use classification
To
group data, use clustering.
To
find value, use regression
Usually classification and regression are considered as supervised
learning.
Clustering and association are unsupervised learning.
Also learn the Difference between supervised, unsupervised and semi
supervised learning with examples and applications.