Scikit-learn cheat sheet: methods for classification & regression

Machine Learning is a fast-growing technology in today’s world. Machine learning is already integrated into our daily lives with tools like face recognition, home assistants, resume scanners, and self-driving cars.

Scikit-learn 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 Scikit-learn, we introduced the basics of this library alongside the most common operations. Today, we take our Scikit-learn knowledge one step further and teach you how to perform classification and regression, followed by the 10 most popular methods for each.

Learn how to use scikit-learn in your ML projects.#

Master the most popular Scikit-learn functions and ML algorithms using interactive examples, all in one place.

Hands-on Machine Learning with Scikit-Learn

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 I am going to 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 as Classes. Since the output is known as classes, the method is called Classification.
• 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 size of the house, location of the house, and no. of floors, etc.

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 F1-score
from sklearn.metrics  import f1_score,accuracy_score
 
#Importing the Decision Tree from scikit-learn 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/hcirs-ctf/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, let’s 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().