Oct 19, 2020 - 6 min read

Aman Anand

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.

**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

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

**Hands-on Machine Learning with Scikit-Learn **

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 **decision-making capabilities**. Machine Learning can be divided into three major categories:

- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning

Types of Machine 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 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 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.

Python provides a lot of tools for implementing Classification and Regression. The most popular open-source Python data science library is scikit-learn. Let’s learn how to use scikit-learn 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

```
#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
```

```
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
```

```
#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 is as simple as this
# Use the function imported above and apply fit() on it
DT= DecisionTreeClassifier()
DT.fit(X_train,y_train)
```

```
# 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()`

.

```
from sklearn.linear_model import LogisticRegression
```

```
from sklearn.svm import SVC
```

```
from sklearn.naive_bayes import GaussianNB
from sklearn.naive_bayes import MultinomialNB
```

```
from sklearn.linear_model import SGDClassifier
```

```
from sklearn.neighbors import KNeighborsClassifier
```

```
from sklearn.tree import DecisionTreeClassifier
```

```
from sklearn.ensemble import RandomForestClassifier
```

```
from sklearn.ensemble import GradientBoostingClassifier
```

```
from lightgbm import LGBMClassifier
```

```
from xgboost.sklearn import XGBClassifier
```

```
from sklearn.linear_model import LinearRegression
```

```
from lightgbm import LGBMRegressor
```

```
from xgboost.sklearn import XGBRegressor
```

```
from catboost import CatBoostRegressor
```

```
from sklearn.linear_model import SGDRegressor
```

```
from sklearn.kernel_ridge import KernelRidge
```

```
from sklearn.linear_model import ElasticNet
```

```
from sklearn.linear_model import BayesianRidge
```

```
from sklearn.ensemble import GradientBoostingRegressor
```

```
from sklearn.svm import SVR
```

I hope this short tutorial and cheat sheet is helpful for your scikit-learn 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 Scikit-learn and the other Python ML libraries.

As you continue your Scikit-learn journey, here are the next algorithms and topics to learn:

- Support Vector machine
- Random Forest
- Cross-validation techniques
`grid_search`

`fit_transform`

`n_clusters`

`n_neighbors`

`sklearn.grid`

To advance your scikit-learn journey, Educative has created the course **Hands-on Machine Learning with Scikit-Learn**. With in-depth explanations of all the Scikit-learn basics and popular ML algorithms, this course offers everything you need in one place. By the end, you’ll know how and when to use each machine learning algorithm and will have the Scikit skills to stand out to any interviewer.

*Happy learning!*

WRITTEN BYAman Anand

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