Machine Learning is a fastgrowing technology in today's world. Machine learning is already integrated into our daily lives with tools like face recognition, home assistants, resume scanners, and selfdriving cars.
Scikitlearn is the most popular Python library for performing classification, regression, and clustering algorithms. It is an essential part of other Python data science libraries like matplotlib
, NumPy
(for graphs and visualization), and SciPy
(for mathematics).
In our last article on Scikitlearn, we introduced the basics of this library alongside the most common operations. Today, we take our Scikitlearn knowledge one step further and teach you how to perform classification and regression, followed by the 10 most popular methods for each.
Today, we will cover:
 Refresher on Machine Learning
 How to implement classification and regression
 10 popular classification methods
 10 popular regression methods
 What to learn next
Refresher on Machine Learning
Machine Learning is teaching the computer to perform and learn tasks without being explicitly coded. This means that the system possesses a certain degree of decisionmaking capabilities. Machine Learning can be divided into three major categories:
 Supervised Learning
 Unsupervised Learning
 Reinforcement Learning
Supervised Learning
In this ML model, our system learns under the supervision of a teacher. The model has both a known input and output used for training. The teacher knows the output during the training process and trains the model to reduce the error in prediction. The two major types of supervised learning methods are Classification and Regression.
Unsupervised Learning
Unsupervised Learning refers to models where there is no supervisor for the learning process. The model uses just input for training. The output is learned from the inputs only. The major type of unsupervised learning is Clustering, in which we cluster similar things together to find patterns in unlabeled datasets.
Reinforcement Learning
Reinforcement Learning refers to models that learn to make decisions based on rewards or punishments and tries to maximize the rewards with correct answers. Reinforcement learning is commonly used for gaming algorithms or robotics, where the robot learns by performing tasks and receiving feedback.
In this post, we will explain the two major methods of Supervised Learning:
Classification: In Classification, the output is discrete data. In simpler words, this means that we are going to categorize data based on certain features. For example, differentiating between Apples and Oranges based on their shapes, color, texture, etc. In this example, shape, color, and texture are known as
features
, and the output is "Apple" or "Orange", which are known asClasses
. Since the output is known as classes, the method is calledClassification
.Regression: In Regression, the output is continuous data. In this method, we predict the trends of training data based on the features. The result does not belong to a certain category or class, but it gives a numeric output that is a real number. For example, predicting House Prices is based on certain features like the size of the house, the location of the house, and the number of floors, etc.
How to implement classification and regression
Python provides a lot of tools for implementing Classification and Regression. The most popular opensource Python data science library is scikitlearn. Letβs learn how to use Scikitlearn to perform Classification and Regression in simple terms.
The basic steps of supervised machine learning include:
 Load the necessary libraries
 Load the dataset
 Split the dataset into training and test set
 Train the model
 Evaluate the model
Loading the Libraries
#Numpy deals with large arrays and linear algebra
import numpy as np
# Library for data manipulation and analysis
import pandas as pd
# Metrics for Evaluation of model Accuracy and F1score
from sklearn.metrics import f1_score,accuracy_score
#Importing the Decision Tree from scikitlearn library
from sklearn.tree import DecisionTreeClassifier
# For splitting of data into train and test set
from sklearn.model_selection import train_test_split
Loading the Dataset
train=pd.read_csv("/input/hcirsctf/train.csv")
# read_csv function of pandas reads the data in CSV format
# from path given and stores in the variable named train
# the data type of train is DataFrame
Splitting into Train & Test set
#first we split our data into input and output
# y is the output and is stored in "Class" column of dataframe
# X contains the other columns and are features or input
y = train.Class
train.drop(['Class'], axis=1, inplace=True)
X = train
# Now we split the dataset in train and test part
# here the train set is 75% and test set is 25%
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=2)
Training the model
# Training the model is as simple as this
# Use the function imported above and apply fit() on it
DT= DecisionTreeClassifier()
DT.fit(X_train,y_train)
Evaluating the model
# We use the predict() on the model to predict the output
pred=DT.predict(X_test)
# for classification we use accuracy and F1 score
print(accuracy_score(y_test,pred))
print(f1_score(y_test,pred))
# for regression we use R2 score and MAE(mean absolute error)
# all other steps will be same as classification as shown above
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import r2_score
print(mean_absolute_error(y_test,pred))
print(mean_absolute_error(y_test,pred))
Now that we know the basic steps for Classification and Regression letβs learn about the top methods for Classification and Regression that you can use in your ML systems. These methods will simplify your ML programming.
Note: Import these methods to use in place of the
DecisionTreeClassifier()
.
10 popular classification methods
Logistic Regression
from sklearn.linear_model import LogisticRegression
Support Vector Machine
from sklearn.svm import SVC
Naive Bayes (Gaussian, Multinomial)
from sklearn.naive_bayes import GaussianNB
from sklearn.naive_bayes import MultinomialNB
Stochastic Gradient Descent Classifier
from sklearn.linear_model import SGDClassifier
KNN (knearest neighbor)
from sklearn.neighbors import KNeighborsClassifier
Decision Tree
from sklearn.tree import DecisionTreeClassifier
Random Forest
from sklearn.ensemble import RandomForestClassifier
Gradient Boosting Classifier
from sklearn.ensemble import GradientBoostingClassifier
LGBM Classifier
from lightgbm import LGBMClassifier
XGBoost Classifier
from xgboost.sklearn import XGBClassifier
10 popular regression methods
Linear Regression
from sklearn.linear_model import LinearRegression
LGBM Regressor
from lightgbm import LGBMRegressor
XGBoost Regressor
from xgboost.sklearn import XGBRegressor
CatBoost Regressor
from catboost import CatBoostRegressor
Stochastic Gradient Descent Regression
from sklearn.linear_model import SGDRegressor
Kernel Ridge Regression
from sklearn.kernel_ridge import KernelRidge
Elastic Net Regression
from sklearn.linear_model import ElasticNet
Bayesian Ridge Regression
from sklearn.linear_model import BayesianRidge
Gradient Boosting Regression
from sklearn.ensemble import GradientBoostingRegressor
Support Vector Machine
from sklearn.svm import SVR
What to learn next
I hope this short tutorial and cheat sheet is helpful for your scikitlearn journey. These methods will make your data scientist journey much smoother and simpler as you continue to learn these powerful tools. There is still a lot to learn about Scikitlearn and the other Python ML libraries.
As you continue your Scikitlearn journey, here are the next algorithms and topics to learn:
 Support Vector machine
 Random Forest
 Crossvalidation techniques

grid_search
fit_transform
n_clusters
n_neighbors
sklearn.grid
To advance your ScikitLearn journey, Educative has created the course Handson Machine Learning with Scikitlearn. With indepth explanations of all the Scikitlearn basics and popular ML algorithms, this course offers everything you need in one place. By the end, youβll know how and when to use each machine learning algorithm and will have the Scikit skills to stand out to any interviewer.
Happy learning!
Continue reading about ML and Scikitlearn on Educative
 Scikitlearn Tutorial: how to implement linear regression
 Crack the top 40 machine learning interview questions
 Pandas Cheat Sheet: top 35 commands and operations
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