Top 6 Supervised Learning Algorithm in Sklearn

What is Supervised Learning? .Well, It is the Data mining process of inferring a function from labeled training data.

Confused? me too.

It is the task on which a functions is aproximated where an input X maps to an output Y.
Let me try again, You have an input X and an output Y, and you know both. For example, let’s say you have a data set with data about people who live in a zip code and their voting history. Using that information, you want to predict if a person in that zip code will be voting next year.

The whole point of supervised learning is about to create a function that maps the relationship between X(input) and Y( the output)

The goal is to approximate the mapping function so close that when you have a new data input, the output is correct.

Now, Supervised Learning can be grouped in:

Classification

A classification problem is when the output variable is a category that you want to attach to the input. For example email spam or not, organ donor or not.

Regression

Now regression is when things get interesting. Now you want to predict real values, not classifying the data.
For example:

• How much money allocated for a specific budget line item?
• Predict economist growth of a state.
• Salary estimation.

Now, lets talk about implementation.

For that we are going to use scikit-learn:

What is it? Well scikit-learn, is a ML library written in Python and Cython that provide several of Machine Learning Algorithms.

Below you can find a few of them within the Supervise Learning domain

Gaussian Naive Bayes (GaussianNB)

Naive Bayes classifier is based on the Baye’s theorem with strong independence assumption between the features.

The theorem describe the probability of an event based on prior knowledge of conditions that might be related.

The classifier makes a very strong assumption on the shape of the data distribution. Any two features are independent given the output class. Because of this the output could be very bad.

For Example :
Taking a nap after eating turkey.

P(nap)=Probability of taking a npa.
P(dinner) = Probability of eating turkey.
P(nap/turkey)=(P(turkey|nap) P(nap))/P(turkey)

P(nap/turkey)=P(turney∣nap)∗P(nap)P(turkey)P(nap/turkey)=\frac{P(turney|nap)*P(nap)}{P(turkey)}P(nap/turkey)=P(turkey)P(turney∣nap)∗P(nap)​

Decision Trees

Ensemble Methods (Bagging, AdaBoost, Random Forest, Gradient Boosting)

K-Nearest Neighbors (KNeighbors)

Stochastic Gradient Descent Classifier (SGDC)

Support Vector Machines (SVM)

Logistic Regression