Supervised Learning System
Machine
Learning systems can be classified according to the amount and
type of supervision they get during training.
There are four major
categories:
i. supervised learning
ii. unsupervised learning
iii. semi supervised learning
iv. Reinforcement learning
Supervised learning
In supervised learning, the training
set you feed to the algorithm
includes the desired solutions, called labels
(Figure 1).
Figure 1. A labeled training set for spam classification (an example of supervised
learning)
A typical supervised learning task is
classification.
The spam filter is a good example of this:
it
is trained with many example emails along with their class (spam or ham), and it must learn how to classify new emails.
Another typical task
is to predict a target numeric
value, such as the price of a car, given a set
of features (mileage, age, brand, etc.) called predictors.
This sort of task is called regression (Figure 2).
Figure 2. A
regression problem: predict a value, given an input feature (there are usually multiple
input features, and sometimes multiple output values)
To train the system, you
need to give it many examples of cars, including both their predictors and their labels (i.e., their prices).
Note that some regression algorithms can be used for
classification as well, and vice versa.
For example, Logistic regression is commonly used
for classification, as it can output a value that corresponds to the
probability of belonging to a given class (e.g., 20% chance of being spam).
Here are some of the most important supervised
learning algorithms:
a. k-Nearest Neighbors
b. Linear Regression
c. Logistic Regression
d. Support Vector Machines (SVMs)
e. Decision Trees and Random Forests
f. Neural networks
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