DSA-C02 Snowflake SnowPro Advanced: Data Scientist Certification

Supervised Learning

Overview:

Supervised learning is a type of machine learning that uses labeled data to train machine learning

models. In labeled data, the output is already known. The model just needs to map the inputs to the

respective outputs.

Algorithms:

Some of the most popularly used supervised learning algorithms are:

· Linear Regression

· Logistic Regression

· Support Vector Machine

· K Nearest Neighbor

· Decision Tree

· Random Forest

· Naive Bayes

Working:

Supervised learning algorithms take labelled inputs and map them to the known outputs, which

means you already know the target variable.

Supervised Learning methods need external supervision to train machine learning models. Hence,

the name supervised. They need guidance and additional information to return the desired result.

Applications:

Supervised learning algorithms are generally used for solving classification and regression problems.

Few of the top supervised learning applications are weather prediction, sales forecasting, stock price

analysis.

Supervised learning methodology applies conditional probability of all the variables with respective

the dependent variable and generally conditional probability of variables is nothing but a basic

method of estimating the statistics for few random experiments.

Conditional probability is thus the likelihood of an event or outcome occurring based on the

occurrence ofsome other event or prior outcome. Two events are said to be independent if one

event occurring does not affect the probability that the other event will occur.