# Supervised Learning In Scikit-Learn

## Hello again!

This is part two of the Scikit-learn series, which is as follows:

- Part 1 – Introduction
- Part 2 – Supervised learning in Scikit-learn (this article)
- Part 3 – Unsupervised Learning in Scikit-learn

## Recap Of Supervised Learning

*So you are already familiar with Supervised learning but those who are not let’s take a quick recap.*

Q. What is supervised learning?

In machine learning, it is a type of system in which both input and desired output data are provided. Input and output data are labeled for classification to provide a learning basis for future data prediction.

```
from sklearn.linear_model import LinearRegression #import statement
clf=LinearRegression() #we created a classifier from an object named LinearRegression.
clf.fit ([[0, 0], [1, 1], [2, 2]], [0, 1, 2]) #fitting a classifier on a data
```

```
clf.coef_ # calculated the slope
```

As you can see there is just a small code that can get you started with this amazing algorithm. Isn’t it amazing? You can even try prediction on the testing set by using ‘.pred’ function.

For more in-depth understanding of this linear model consider trying yourself by taking an example.

An easy example can be found here :

http://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py

```
from sklearn import svm
X = [[0, 0], [1, 1]] # dataset
y = [0, 1]
clf = svm.SVC() # classifier is created
clf.fit(X, y) # fitting classifier on dataset
```

The parameters you see in the brackets can be changed according to the dataset you have been given.

Once you are comfortable writing the above-mentioned code try yourself by tweaking the parameters.

```
clf.predict([[1., 0.]]) # predicting values
```

```
from sklearn.linear_model import SGDClassifier
X = [[0., 0.], [1., 1.]]
y = [0, 1]
clf = SGDClassifier(loss="hinge", penalty="l2") #hyperparameters
clf.fit(X, y)
```

## Naive Bayes In Sklearn

Naive Bayes classifier calculates the probabilities for every factor. Then it selects the outcome with the highest probability.

This classifier assumes the features are independent. Thus the word ‘naive’ is used.

It is one of the most common algorithms in machine learning.

Code :

```
from sklearn import datasets
iris = datasets.load_iris() # loading the dataset
from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()
y_pred = gnb.fit(iris.data, iris.target).predict(iris.data) #fitting and predicting on same line
```

## Decision Tree Regression In Sklearn

Decision Trees is another type of supervised machine learning algorithm where the data is continuously split according to a certain parameter.

More the data more is the accuracy of the model.

Decision trees is one of the most used algorithms out of all supervised learning algorithms and finds huge applications in the industry.

Code :

```
from sklearn import tree
X = [[0, 0], [1, 1]]
Y = [0, 1]
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X, Y)
clf.predict([[2., 3.]])
```

## Ensemble Methods In SkLearn

It contains bagging methods and random forests.

### Random Forests

Another powerful machine learning algorithm that produces great results even without hyper-parameter tuning.

It is also one of the most used algorithms, because of its simplicity and the fact that it can be used for both classification and regression tasks.

Code :

```
from sklearn.ensemble import RandomForestClassifier
X = [[0, 0], [1, 1]]
Y = [0, 1]
clf = RandomForestClassifier(n_estimators=10)
clf = clf.fit(X, Y)
```

## What have we Learnt?

By now we have learned how to implement each supervised algorithm using scikit-learn.

Stil there are many features that each algorithm has in scikit-learn which can be mastered only by practicing.

So stop wasting your time and head straight onto the official documentation of scikit-learn for supervised algorithms and make sure you understand each algorithm mathematically as well as by practicing on different datasets.

Link :

http://scikit-learn.org/stable/supervised_learning.html

Note: **Next part of this series is on unsupervised learning, so make sure you don’t miss that: **Part 3 – Unsupervised Learning in Scikit-learn

### Deepanshu Gaur

Loves to learn new technologies and this attitude keeps me going.

Fast learner.

Strong foundation in data structures & algorithms.

https://www.linkedin.com/in/deepanshu-g-37a42899

#### Latest posts by Deepanshu Gaur (see all)

- Scikit Learn – Part 1 – Introduction - February 20, 2019
- Scikit Learn – Part 2 – Supervised Learning - January 8, 2019
- Scikit Learn – Part 3 – Unsupervised Learning - December 8, 2018