## What is Supervised Machine Learning? || Supervised Machine Learning techniques and algorithms.

supervised machine learning |

various machine learning mathematical techniques to train the machine using data which is already labeled. someone can build supervised machine learning

algorithms from labeled training data to predict the same.

Some of the examples of supervised machine learning systems are self-driving cars,

expert systems, facial recognition programs, chatbots and robots which support future judgments.

following are the mathematical techniques that are used in building supervised

machine-learning algorithms.

**1) Linear Regression**

**2) Logistic Regression**

**3) Decision Tree**

**4) Random forest**

**5) support vector machine**

### Linear Regression:

The linear regression technique uses a linear mathematical formula/technique to predict the dependent

variable or output value. let us consider the following area vs price linear graph in order to understand

the Linear Regression technique.

i.e

**y=m*x + b**

where y=dependent variable

x=independent vairable

m=slope

c=y-intercept.

Here the area is the independent variable which is plotted on the x-axis and price is the dependent variable plotted on the y-axis. And the value of the dependent variable (i.e price) is predicted by an above linear mathematical formula i.e y=m*x + b.

Linear Regression is further classified into single variable Linear Regression and

multiple variables linear Regression depending upon the number of independent variables.

### Logistic Regression:

logistic regression method uses logit mathematical function i.e

**f(x) = 1/(1 + exp(-x))**
which helps to predict the probability of occurrence of an event by fitting data to this

logit function/(

*f(x) = 1/(1 + exp(-x))*). And this logit function always predict the output value whose values lies in between
0 and 1.

Logistic Regression further classified into binary classification and multiclass

classification

the binary classification has only two distinct classes i.e binary classification

prediction is like true or false i.e it has only two options.

And selecting between more than two classes is called multiclass classification.

### Decision Tree:

the decision tree technique is a flowchart in which test on a feature is represented

by each internal node and each leaf node represents class label and branches

represent conjunctions of features that lead to those class labels.

and the syntax for the same looks like as above.

### Random Forest:

the supervised machine learning algorithm which is used for both classification

as well as regression is known as random forest

The basic syntax of the Random Forest algorithm is explained above with some code programmed in

anaconda navigator

### Support Vector Machine:

support vector machine is another supervised machine learning technique which is used for solving

highly non-linear problem using a kernel technique

support vector machine is abbreviated as SVM which is also known as a support vector network- a very powerful method of solving both linear and regression problems. In short, SVM is a discriminative

classifier which aims at finding decision boundaries that separate observations with differing class

memberships

the basic syntax of the support vector machine algorithm is shown above which is designed/programmed

in anaconda navigator.

Well explained... Easy to understand. . Thanks a lot for sharing your knowledge toward data science

ReplyDeleteThanks for the well-written post and I will follow your updates regularly.

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