Scikit-learn for Machine Learning

Scikit-learn is a powerful library that provides a wide range of tools for data preprocessing and machine learning. It is built on top of other popular Python libraries like NumPy, SciPy, and Matplotlib, making it an integral part of the Python ecosystem for Data Science and Machine Learning. If you want to learn Scikit-learn for Machine Learning, this article is for you. In this article, I will take you through a practical guide to Scikit-learn for Machine Learning.

What is Scikit-learn?

Scikit-learn, also known as sklearn, is an open-source Python library built on top of other popular Python libraries like NumPy, SciPy, and Matplotlib, making it an integral part of the Python ecosystem for Data Science and Machine Learning.

Below are some features that Scikit-learn provides for working with data and Machine Learning algorithms:

  1. Simple and Consistent API: Scikit-learn provides a straightforward and consistent API that is easy to use. This simplicity allows data scientists to quickly prototype machine learning models and experiments.
  2. Wide Range of Algorithms: The library includes a rich collection of machine learning algorithms for tasks like classification, regression, clustering, dimensionality reduction, and more. This diversity of algorithms ensures that data scientists can choose the most appropriate one for their specific problem.
  3. Efficient Data Preprocessing: Scikit-learn offers tools for data preprocessing, including data normalization, scaling, encoding categorical variables, and handling missing values. These functionalities are crucial for preparing data for machine learning models.
  4. Model Evaluation and Selection: Data scientists can evaluate the performance of machine learning models using Scikit-learn’s metrics and cross-validation techniques. It helps in selecting the best model for a given task and avoiding overfitting.
  5. Feature Selection and Extraction: Scikit-learn provides methods for feature selection and extraction, allowing data scientists to focus on the most relevant features and improve model efficiency.

I hope you have understood what Scikit-learn is and how it helps. In the section below, I’ll take you through a practical guide to Scikit-learn for Machine Learning.

Scikit-learn for Machine Learning

Let’s start this guide to Scikit-learn by importing Scikit-learn and all the essential classes we need:

# Import Scikit-learn library
from sklearn import datasets, model_selection, preprocessing, metrics

Before getting started, I’ll import the popular Iris data:

# Load a dataset
data = datasets.load_iris()

# Separate features and target variable
X =  # Features
y =  # Target variable

Now, let’s see how we can split the data using Scikit-learn:

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.3, random_state=42)

Here, we are splitting our dataset into two distinct subsets:

  1. a training set
  2. a testing set

The X variable contains our feature data, and y contains the corresponding target labels. By using the train_test_split function from Scikit-learn’s model_selection module, we allocate 70% of the data to X_train and y_train, which will be used for training our machine learning model. The remaining 30% of the data is assigned to X_test and y_test, which are reserved for evaluating the model’s performance. 

The test_size=0.3 parameter specifies the proportion of data to assign to the testing set, and random_state=42 ensures that the split is reproducible, meaning it will yield the same results if executed with the same random seed, facilitating consistency in experimentation and evaluation.

Now, let’s see how to preprocess the data using Scikit-learn:

# Standardize features
scaler = preprocessing.StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

Here, we are performing a data preprocessing step called feature standardization using Scikit-learn’s StandardScaler from the preprocessing module. Feature standardization is an optional but often recommended step in machine learning. It involves transforming the numerical features in both the training (X_train) and testing (X_test) datasets so that they have a mean of 0 and a standard deviation of 1. 

This process helps ensure that all features contribute equally to the model, prevents some features from dominating others, and can improve the performance and stability of various machine learning algorithms, particularly those sensitive to the scale of input features, such as support vector machines and k-nearest neighbours.

Now, the next step is to choose a Machine Learning algorithm:

# Import the model you want to use (e.g., Decision Tree)
from sklearn.tree import DecisionTreeClassifier

# Create an instance of the model
model = DecisionTreeClassifier()

Here, we are selecting and preparing a Machine Learning model for our task. First, we import the DecisionTreeClassifier class from Scikit-learn’s tree module, which is a specific algorithm for classification tasks. Next, we create an instance of the DecisionTreeClassifier class and assign it to the variable model. This instance represents our machine learning model, specifically a decision tree classifier, which we will use to learn patterns in the training data and make predictions on new data points. 

The choice of the machine learning algorithm (in this case, a decision tree) depends on the nature of your problem, and Scikit-learn provides a wide range of models for various types of tasks. You can learn more about choosing Machine Learning algorithms from here.

Now, the next step is training the model:

# Train the model on the training data, y_train)

Here, we are training our machine learning model using the training data. The fit method is a fundamental operation in Scikit-learn that takes the features (X_train) and corresponding target labels (y_train) and teaches the model to understand the relationships between the features and the target variable. In the case of a decision tree classifier, this means constructing a tree structure that can make predictions based on the patterns it learns from the training data.

Essentially, this step is where the model learns to make predictions by finding patterns, rules, or decision boundaries that map input features to the correct target labels. Once the training is complete, the model is ready to make predictions on new, unseen data. You can learn more about using other Machine Learning algorithms for model training from here.

Now, the next step is to make predictions:

# Make predictions on the test data
y_pred = model.predict(X_test)
[1 0 2 1 1 0 1 2 1 1 2 0 0 0 0 1 2 1 1 2 0 2 0 2 2 2 2 2 0 0 0 0 1 0 0 2 1 0 0 0 2 1 1 0 0]

Here, we are using our trained machine learning model to make predictions on the testing data. After training the model, we apply it to the previously unseen test dataset (X_test) using the predict method. This method takes the test data as input and generates predicted target labels (y_pred) based on the patterns and rules the model learned during training.

These predicted labels represent the model’s best guesses for the target values of the test data.

Now, the next step is to evaluate the model:

# Calculate accuracy
accuracy = metrics.accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy}")
Accuracy: 1.0

Here, we are calculating the accuracy of our machine learning model’s predictions on the testing data. The accuracy_score function from Scikit-learn’s metrics module is used to compare the predicted labels (y_pred) generated by our model with the actual true labels from the testing data (y_test). It computes the fraction of correctly predicted labels out of all the labels in the testing dataset. The result is stored in the accuracy variable and printed out to provide a quantitative measure of how well our model performs on the test data.

An accuracy value close to 1.0 indicates that the model’s predictions closely match the true labels, while lower values suggest lower predictive performance. You can learn more about other performance evaluation methods from below:

  1. Classification Metrics
  2. Regression Metrics

So this is how you can use Scikit-learn for Machine Learning. You can learn many more Machine Learning concepts and algorithms from my book on Machine Learning Algorithms.


Scikit-learn, also known as sklearn, is an open-source Python library built on top of other popular Python libraries like NumPy, SciPy, and Matplotlib, making it an integral part of the Python ecosystem for Data Science and Machine Learning. I hope you liked this article on a practical guide to Scikit-learn for Machine Learning. Feel free to ask valuable questions in the comments section below.

Aman Kharwal
Aman Kharwal

I'm a writer and data scientist on a mission to educate others about the incredible power of data📈.

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