Scikit Learn

Scikit learn is the powerful tool of machine learning.it provides the statistical modelling,classification,regression etc.This library is related to Python and also developed in Python Languages.

It is the free library,Scikit also support the functionality of the Numpy and Scipy,

Benefit of Scikit Learn

Consistent interface to machine learning model
Provide many tuning parameter but with the sensible default
Exceptional documentation
Rich set of Functionality for companion task
Active community for Development and support

Installation

Type the following command to into your command shell to install Scikit Learn

pip install -U scikit-learn

Using Conda

conda install scikit-learn

Load the Dataset

Data set is the collection of the data,which have two type:

Feature : it is known as the predictive value,This is our simple variable which represents the feature matrix.
Reponse : It is known as the target value,the result of the response depends upon the feature variable.

Loading internal Dataset
Program

# iris dataset
from sklearn.datasets import load_iris 
irise = load_iris() 
  
# feature   matrix is x and vector response y
a= iris.data 
b = iris.target 
  
# store the feature and target names 
fnames = iris.feature_names 
tnames = iris.target_names 
  
# print the data set
print("Feature names:", fnames) 
print("Target names:", tnames) 
  
# a and b are numpy arrays 
print("type is:", type(a)) 
  
# printing first 5 input rows 
print("10 rows of a", a[:10])

Output

Feature names: ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']
Target names: ['setosa' 'versicolor' 'virginica']
type is: 
10 rows of a [[5.1 3.5 1.4 0.2]
 [4.9 3.  1.4 0.2]
 [4.7 3.2 1.3 0.2]
 [4.6 3.1 1.5 0.2]
 [5.  3.6 1.4 0.2]
 [5.4 3.9 1.7 0.4]
 [4.6 3.4 1.4 0.3]
 [5.  3.4 1.5 0.2]
 [4.4 2.9 1.4 0.2]
 [4.9 3.1 1.5 0.1]]

Program

# loading iris dataset
from sklearn.datasets import load_iris 
irise = load_iris() 
  
# feature matrix variable is (a) and vector response is (b)
a = iris.data 
b = iris.target 
  

from sklearn.model_selection import train_test_split 
a_train, a_test, b_train, b_test = train_test_split(a, b, test_size=0.6, random_state=5) 
  
# print shapes of the a
print(a_train.shape) 
print(a_test.shape) 
  
# printing the shapes of b
print(b_train.shape) 
print(b_test.shape)

Output

(60, 4)
(90, 4)
(60,)
(90,)

Data Science

Scikit Learn