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Practice

A Library to Start Machine Learning Easily, Scikit-Learn

Machine learning involves recognizing patterns in data and making predictions. However, implementing algorithms from scratch can be challenging and time-consuming.

Scikit-Learn is a Python-based machine learning library that helps you implement machine learning models easily with just a few lines of Python code.


Installing Scikit-Learn​

You can install Scikit-Learn with the following command. In this practice environment, Scikit-Learn is already installed, so there's no need for separate installation.

Installing Scikit-Learn
pip install scikit-learn

Why Scikit-Learn is Widely Used​

Scikit-Learn is one of the most widely used machine learning libraries for AI beginners.

Here are some reasons why Scikit-Learn is popular:

  1. Ease of Use : You can learn and predict machine learning models with just a few lines of code.

  2. Variety of Algorithms : It supports various algorithms like linear regression, decision trees, random forests, support vector machines, and more.

  3. Unified API : It provides methods like fit(), predict(), and score() to handle trained machine learning models easily.

  4. Data Pre-processing Support : It offers various pre-processing functions such as handling missing values, feature scaling, and one-hot encoding.


Example Usage of Scikit-Learn​

Let's create a simple Supervised Learning model using Scikit-Learn.

Supervised learning is a method where you use input data and output labels to train the model.

Detailed content related to machine learning will be covered later. For this lesson, just take a look at the overall code.

The following example shows the process of learning and predicting with a simple dataset using the DecisionTreeClassifier.

Basic Example of Scikit-Learn
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris

# Load the dataset
iris = load_iris()
X, y = iris.data, iris.target

# Split into training and testing data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create and train the model
model = DecisionTreeClassifier()
model.fit(X_train, y_train)

# Perform predictions
predictions = model.predict(X_test)

# Evaluate the model
accuracy = model.score(X_test, y_test)
print(f"Model Accuracy: {accuracy:.2f}")

Code Explanation​

  • load_iris(): Loads the Iris dataset.

  • train_test_split(): Splits into training and testing data.

  • DecisionTreeClassifier(): Creates a decision tree model.

  • fit(): Trains the model.

  • predict(): Performs predictions on the test data.

  • score(): Evaluates the accuracy of the model.


Scikit-Learn is a powerful library that helps you implement machine learning easily in Python.

You can learn and evaluate various machine learning models with simple code, and it also supports data pre-processing features.

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