Practical How To Guide To Binary Classification [PyTorch, Keras, Scikit-Learn]

by | Oct 9, 2023 | Data Science, Machine Learning

Binary classification is a fundamental concept in machine learning, and it serves as the building block for many other classification tasks. In this section, we’ll explore the intricacies of binary classification, its fundamental characteristics, real-world applications, and its role in predictive modelling.

What is binary classification in machine learning?

Binary classification involves categorizing data into one of two possible classes or categories based on specific characteristics or features. These classes are typically denoted as “positive” and “negative,” “yes” and “no,” or “1” and “0.” The primary goal of binary classification is to predict the class label of new data points based on patterns learned from a labelled dataset.

Key aspects of binary classification are:

  • Two Classes: As the name suggests, binary classification deals with precisely two distinct classes. These classes are often referred to as the “positive” class, and the “negative” class, where the positive class represents the presence or occurrence of something (e.g., a disease, fraud, or an event), and the negative class represents its absence.
  • Predictive Outcome: The primary objective of binary classification is to predict which of the two classes a given data point belongs to. This prediction is typically expressed as a probability score or a binary decision (e.g., “spam” or “not spam”).

Binary classification algorithms learn from historical data, where each data point is associated with a known class label. During training, the algorithm identifies patterns and relationships within the data, enabling it to make predictions for unseen data.

Real-world applications of binary classification

Binary classification has a wide range of practical applications across various industries. Here are a few examples that illustrate its significance:

1. Spam Detection

Spam email filters use binary classification to categorize incoming emails as “spam” or “not spam.” By analyzing email content, sender information, and other features, the filter can automatically route unwanted messages to the spam folder, ensuring that users’ inboxes remain clutter-free.

2. Disease Diagnosis

In medical diagnostics, binary classification determines whether a patient has a particular medical condition or disease. For instance, a machine learning model can analyze patient data (e.g., symptoms, lab results, medical history) and classify patients into “healthy” or “diseased” categories, assisting healthcare professionals in making accurate diagnoses.

3. Customer Churn Prediction

Businesses often use binary classification to predict whether customers will likely churn (i.e., stop using their services). By analyzing customer behaviour, transaction history, and interaction data, companies can proactively identify at-risk customers and take measures to retain them.

4. Fraud Detection

In the finance industry, binary classification is instrumental in identifying fraudulent transactions. Algorithms can assess various transaction attributes, such as transaction amount, location, and user behaviour, to classify transactions as “legitimate” or “fraudulent,” thereby helping prevent financial losses.

Binary classification can be used for fraud detection

5. Sentiment Analysis

Binary classification is frequently used in natural language processing tasks like sentiment analysis. In this context, it classifies text or comments as “positive” or “negative” sentiments, allowing businesses to gauge public opinion and customer feedback.

These real-world applications highlight the versatility and practicality of binary classification. It is a fundamental tool for automating decision-making processes and making predictions in various domains.

Binary classification is a core concept in machine learning, where data is categorized into one of two classes based on learned patterns from labelled examples. Its applications span numerous industries, facilitating automated decision-making and predictive modelling in various domains. Understanding the principles and applications of binary classification provides a strong foundation for more complex classification tasks.

Binary classification model selection

Selecting the appropriate machine learning algorithm is a pivotal step in binary classification. The choice of the model can significantly impact the performance and effectiveness of your classification task. In this section, we’ll explore popular algorithms used for binary classification, considerations for choosing the right one, and the importance of hyperparameter tuning.

Top 5 algorithms for binary classification

Several machine learning algorithms are well-suited for binary classification tasks. Each has its strengths, weaknesses, and suitability for specific data types. Here are some of the most commonly used algorithms:

1. Logistic Regression

Logistic Regression is a straightforward and interpretable algorithm used for binary classification. Using a logistic function, it models the probability that a given input belongs to the positive class.

Strengths: Simplicity, interpretability, and ability to provide probability estimates.

2. Decision Trees

Decision Trees are versatile and interpretable algorithms that can be used for both classification and regression tasks. They make decisions by recursively splitting the data based on feature values.

Strengths: Can handle various data types, capture non-linear relationships, and provide clear decision boundaries.

3. Random Forest

Random Forest is an ensemble learning method that combines multiple decision trees to improve classification accuracy. It works by aggregating the predictions of multiple trees.

Strengths: Robust to overfitting, high accuracy, and effective with high-dimensional data.

4. Support Vector Machines (SVM)

Support Vector Machines aim to find a hyperplane that best separates the two classes while maximizing the margin between them. They can handle both linear and non-linear classification tasks using kernel functions.

Strengths: Effective in high-dimensional spaces, works well with small to medium-sized datasets, and can capture complex decision boundaries.

5. Neural Networks

Neural Networks, particularly deep learning models, have gained popularity recently for their ability to handle complex data and learn intricate patterns. In binary classification, a neural network typically has an output layer with a single neuron, producing a probability score.

Strengths: High flexibility, ability to capture complex relationships, and state-of-the-art performance on various tasks.

What factors should you consider when selecting a model?

Choosing the right model for your binary classification task is not a one-size-fits-all decision. Consider the following factors when making your selection:

1. Nature of the Data

  • Data Type: Is your data numerical, categorical, or a mix? Some algorithms are better suited for specific data types.
  • Data Size: The size of your dataset can influence the choice of model. Deep learning models may require large datasets to perform well, whereas simpler models like logistic regression can work with smaller datasets.

2. Model Complexity

  • Complexity vs. Interpretability: Consider the trade-off between model complexity and interpretability. Simpler models like logistic regression are more interpretable, while complex models like neural networks may provide higher accuracy at the cost of interpretability.

3. Domain Knowledge

  • Domain Expertise: Your knowledge of the problem domain can guide model selection. Some domains may have well-established models that are commonly used.

4. Computational Resources

  • Hardware and Software: Ensure you can access the necessary computational resources, such as GPUs or specialized libraries, to train and deploy specific models, particularly deep learning models.

5. Benchmarking

  • Benchmarking: It’s often a good practice to benchmark multiple models to determine which performs best on your dataset. Cross-validation can help assess their performance reliably.

Hyperparameter tuning

Once you’ve selected a model, it’s essential to fine-tune its hyperparameters to achieve optimal performance. Hyperparameters are settings not learned from the data but set prior to training. Common hyperparameters include learning rates, regularization strength, and tree depth (for decision trees and random forests). Techniques like grid or random search can help you systematically explore hyperparameter combinations to find the best configuration.

Model selection is a critical step in binary classification. It involves choosing an algorithm well-suited to your data, problem domain, and available resources. Additionally, hyperparameter tuning is vital in optimizing the selected model’s performance. By carefully considering these factors, you can build a robust binary classification model that accurately predicts class labels for new data points.

Model training

Model training is a crucial step in the binary classification process where the selected machine learning algorithm learns from the labelled training data to make predictions about new, unseen data. This section will explore the various aspects of model training, including training data, loss functions, optimization techniques, and evaluation metrics.

Using training data

The heart of model training is utilizing the training dataset, consisting of labelled examples representing positive and negative classes. The model learns to recognize patterns and relationships within this dataset, enabling it to make predictions on unseen data.

  • Training Dataset: This dataset typically includes feature vectors (input data) and corresponding class labels (output or target variable). For binary classification, these class labels are binary (0 or 1).
  • Feature Extraction/Engineering: The feature vectors in the training dataset should be preprocessed and organized to represent the data so the model can understand. This might involve feature scaling, one-hot encoding, or other transformations.

Loss functions

Loss functions measure the discrepancy between the model’s predictions and the actual class labels in the training data. The choice of an appropriate loss function depends on the nature of the problem and the machine learning algorithm being used. For binary classification, common loss functions include:

1. Binary Cross-Entropy Loss (Log Loss)

Overview: Binary cross-entropy loss is common for logistic regression and neural network models. It quantifies the difference between predicted probabilities and actual class labels.

Use Case: Well-suited for binary classification problems where the goal is to estimate probabilities of the positive class.

2. Hinge Loss

Overview: Support Vector Machines (SVMs) often use hinge loss. It encourages correct classification while penalizing instances within a certain margin of the decision boundary.

Use Case: Effective for SVM-based binary classifiers.

3. Gini Impurity

Overview: Gini impurity is used in decision tree-based models, such as random forests. It measures the disorder or impurity in a node’s class distribution.

Use Case: Suitable for decision tree-based binary classification.

The choice of loss function can affect the model’s training dynamics and convergence. Selecting a loss function that aligns with your model’s architecture and the objectives of your binary classification problem is essential.

Optimization techniques

The model’s parameters are iteratively adjusted during training to minimize the chosen loss function. This process is typically performed using optimization techniques. Some common optimization algorithms include:

1. Gradient Descent

Gradient descent is a fundamental optimization technique that minimizes the loss function by iteratively adjusting model parameters toward the steepest descent.

Variants: There are variants of gradient descent, including stochastic gradient descent (SGD), mini-batch gradient descent, and Adam, which adaptively adjusts learning rates.

Illustration of batch gradient descent

2. Newton’s Method

Newton’s method is an optimization technique that uses second-order derivatives (Hessian matrix) to find the optimal parameter values.

Use Case: Suitable for convex optimization problems but may be computationally expensive.

3. Conjugate Gradient

Conjugate gradient is an iterative optimization method that can optimize quadratic objective functions.

Use Case: Effective for optimizing SVMs with certain kernel functions.

The choice of an optimization algorithm can impact the convergence speed and stability of the training process. Experimentation and tuning of hyperparameters related to optimization are often necessary.

Evaluation metrics

To assess the performance of a binary classification model, you need to use appropriate evaluation metrics that measure its effectiveness in making predictions. Common evaluation metrics for binary classification include:

1. Accuracy

The proportion of correctly classified instances out of the total.

Use Case: Provides a general measure of classification performance but may be misleading in the presence of imbalanced datasets.

2. Precision and Recall

Precision: Measures the proportion of true positive predictions among all positive predictions.

Recall (Sensitivity): Measures the proportion of true positive predictions among all actual positives.

Use Case: Useful when you want to focus on minimizing false positives (precision) or ensuring that all positives are correctly identified (recall).

3. F1-Score

The harmonic mean of precision and recall, offering a balanced evaluation metric.

Use Case: A good choice when there is an uneven class distribution or when precision and recall are essential.

4. ROC-AUC (Receiver Operating Characteristic – Area Under the Curve)

Measures the ability of the model to distinguish between positive and negative classes across different thresholds.

Use Case: Provides insights into the overall discriminatory power of the model.

5. Confusion Matrix

A matrix that summarizes the model’s performance, showing the counts of true positives, true negatives, false positives, and false negatives.

Use Case: Useful for a detailed understanding of model errors and trade-offs between evaluation metrics.

The choice of evaluation metric depends on the specific goals of your binary classification problem. For example, in a medical diagnosis scenario, recall may be more critical than precision to ensure that all disease cases are correctly identified, even if it means some false positives.

Model training is a central component of binary classification, where machine learning algorithms learn from labelled data to make predictions. Key considerations during training include the choice of loss functions, optimization techniques, and evaluation metrics, which should align with the nature of the problem and the objectives of your binary classification task. You can build a well-performing binary classification model by carefully selecting and tuning these components.

How to implement binary classification in Python

How to implement binary classification with Pytorch

Binary classification using PyTorch involves creating and training a neural network for tasks where the goal is to classify input data into one of two classes. Below, I’ll provide a step-by-step guide on how to perform binary classification in PyTorch.

Note: Before you start, make sure you have PyTorch installed. You can install it using pip:

pip install torch

Now, let’s go through the binary classification process:

Step 1: Import libraries

import torch 
import torch.nn as nn 
import torch.optim as optim 
import numpy as np from sklearn.model_selection 
import train_test_split 

Step 2: Prepare data

Load or generate your dataset and split it into training and testing sets:

# Sample data (replace with your data)
X = np.random.rand(100, 2)
y = (X[:, 0] + X[:, 1] > 1).astype(int)  # Binary classification target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Convert data to PyTorch tensors
X_train = torch.FloatTensor(X_train)
y_train = torch.FloatTensor(y_train)
X_test = torch.FloatTensor(X_test)
y_test = torch.FloatTensor(y_test)

Step 3: Define the model

Create a neural network model for binary classification. Here, we’ll use a simple feedforward neural network:

class BinaryClassifier(nn.Module):
    def __init__(self, input_size):
        super(BinaryClassifier, self).__init__()
        self.fc1 = nn.Linear(input_size, 64)  # Input size to 64 neurons
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(64, 1)  # 64 neurons to 1 output (binary classification)

    def forward(self, x):
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        return torch.sigmoid(x)

Step 4: Create a model, loss function, and optimizer

input_size = X_train.shape[1]
model = BinaryClassifier(input_size)

criterion = nn.BCELoss()  # Binary Cross-Entropy Loss
optimizer = optim.Adam(model.parameters(), lr=0.001)  # Adam optimizer

Step 5: Training

Train the model on the training data:

num_epochs = 1000

for epoch in range(num_epochs):
    # Forward pass
    outputs = model(X_train)
    loss = criterion(outputs, y_train.view(-1, 1))

    # Backpropagation and optimization

    if (epoch + 1) % 100 == 0:
        print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')

# Model training complete

Step 6: Evaluation

Evaluate the model on the test data:

with torch.no_grad():
    predictions = model(X_test)
    predictions = (predictions > 0.5).float()

accuracy = torch.sum(predictions == y_test.view(-1, 1)).item() / y_test.shape[0]
print(f'Accuracy: {accuracy:.2f}')

This code provides a basic outline of binary classification using PyTorch. You can customize the model architecture, hyperparameters, and data preprocessing based on your specific task and dataset. Additionally, consider adding validation sets and early stopping to improve model performance and prevent overfitting.

How to implement binary classification with Keras

Binary classification using Keras, a popular deep learning library in Python, involves creating and training a neural network for tasks where the goal is to classify input data into one of two classes. Below, we will provide a step-by-step guide on how to perform binary classification in Keras.

Note: Make sure you have Keras installed. You can install it using pip:

pip install keras

Now, let’s go through the binary classification process:

Step 1: Import libraries

import numpy as np 
import keras from keras.models 
import Sequential from keras.layers 
import Dense from sklearn.model_selection 
import train_test_split 

Step 2: Prepare data

Load or generate your dataset and split it into training and testing sets:

# Sample data (replace with your data)
X = np.random.rand(100, 2)
y = (X[:, 0] + X[:, 1] > 1).astype(int)  # Binary classification target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Step 3: Define the model

Create a neural network model for binary classification. Here, we’ll use a simple feedforward neural network:

model = Sequential()
model.add(Dense(64, input_dim=X_train.shape[1], activation='relu'))
model.add(Dense(1, activation='sigmoid'))  # Output layer with a sigmoid activation function for binary classification

Step 4: Compile the model

Compile the model by specifying the loss function, optimizer, and evaluation metric:

model.compile(loss='binary_crossentropy', # Binary cross-entropy 
              loss optimizer='adam', # Adam optimizer 

Step 5: Training

Train the model on the training data:

batch_size = 32

history =, y_train, 
                    validation_split=0.1,  # Optional validation split
                    verbose=0)  # Set verbose to 1 for training progress

Step 6: Evaluation

Evaluate the model on the test data:

loss, accuracy = model.evaluate(X_test, y_test)
print(f'Loss: {loss:.4f}, Accuracy: {accuracy:.2f}')

This code provides a basic outline of binary classification using Keras. You can customize the model architecture, hyperparameters, and data preprocessing based on your specific task and dataset. Additionally, consider adding callbacks for early stopping or model checkpointing to improve model performance and prevent overfitting.

How to implement binary classification with scikit-learn

For our last binary classification example using scikit-learn, we will generate a synthetic dataset and then perform binary classification using Python and scikit-learn.

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, confusion_matrix

# Generate synthetic binary classification data

# Create two features (X1 and X2) with random values
X1 = np.random.normal(0, 1, 1000)
X2 = np.random.normal(0, 1, 1000)

# Create a binary target variable (y) based on a decision boundary
decision_boundary = 0.5
y = (X1 + X2 > decision_boundary).astype(int)

# Create a Pandas DataFrame
data = pd.DataFrame({'X1': X1, 'X2': X2, 'Target': y})

# Split the data into features (X) and labels (y)
X = data[['X1', 'X2']]
y = data['Target']

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

# Feature scaling (standardization)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# Create a logistic regression model
model = LogisticRegression(random_state=42)

# Train the model on the training data, y_train)

# Make predictions on the test set
y_pred = model.predict(X_test)

# Calculate evaluation metrics
accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred)
recall = recall_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)
roc_auc = roc_auc_score(y_test, y_pred)

# Print the evaluation metrics
print(f'Accuracy: {accuracy:.2f}')
print(f'Precision: {precision:.2f}')
print(f'Recall: {recall:.2f}')
print(f'F1-Score: {f1:.2f}')
print(f'ROC-AUC: {roc_auc:.2f}')

# Print the confusion matrix
confusion = confusion_matrix(y_test, y_pred)
print('Confusion Matrix:')

In this example, we generate two random features (X1 and X2) and create a binary target variable (Target) based on a decision boundary. We then split the data into training and testing sets, perform feature scaling, train a logistic regression model, make predictions, and evaluate the model’s performance using various metrics.

Going one step further — dealing with imbalanced data

Dealing with imbalanced data is a common challenge in machine learning, particularly in binary classification tasks where one class significantly outnumbers the other. In this section, we’ll explore the issues of imbalanced datasets, various strategies to address the imbalance, and the importance of choosing the correct technique for your problem.

Understanding imbalanced data

Imbalanced data occurs when one class (the minority class) is significantly underrepresented compared to the other class (the majority class). This imbalance can lead to several problems:

  • Biased Models: Machine learning models trained on imbalanced data tend to be biased towards the majority class, aiming to minimize the overall error.
  • Poor Generalization: Models may perform well on the majority class but generalize poorly to the minority class, resulting in a high rate of false negatives.
  • Misleading Metrics: Accuracy, the most common evaluation metric, can be tricky in imbalanced settings. A model that predicts the majority class for all samples can achieve high accuracy but provides no real value.

Strategies for dealing with imbalanced data

1. Resampling

Oversampling: Increase the number of instances in the minority class by duplicating existing samples or generating synthetic samples. Techniques like the Synthetic Minority Over-sampling Technique (SMOTE) can help.

Undersampling: Reduce the number of instances in the majority class by randomly removing samples. Be cautious about information loss when using undersampling.

2. Algorithm-Level Solutions

Cost-sensitive Learning: Modify the learning algorithm to consider class imbalance by assigning different misclassification costs to different classes. Many algorithms in Scikit-learn support this.

Ensemble Methods: Use ensemble methods like Balanced Random Forests or EasyEnsemble, which create multiple models and combine their predictions to address the class imbalance.

3. Anomaly Detection

Treat the minority class as an anomaly detection problem, where the goal is to identify rare instances. Techniques like Isolation Forest or One-Class SVMs can be employed.

4. Data-Level Solutions

Collect more data for the minority class if possible. A larger dataset can help models better understand the minority class.

Create new features or engineer existing ones that help the model distinguish between the classes more effectively.

5. Evaluation Metrics

Rely on evaluation metrics less sensitive to class imbalance, such as precision, recall, F1-score, the area under the Precision-Recall curve (AUC-PR), or the Matthews correlation coefficient (MCC).

Choosing the right strategy

The choice of strategy for dealing with imbalanced data depends on the specific problem, dataset size, and domain knowledge. It may involve resampling techniques, algorithm-level adjustments, and careful evaluation.

Remember that there is no one-size-fits-all solution, and it’s essential to experiment with different approaches to find the most effective strategy for addressing class imbalance in your binary classification task.


Here are the key takeaways:

  • Understanding Binary Classification: Binary classification is a fundamental machine learning task where models learn to classify data into one of two categories or classes.
  • Model Selection: Choose the correct algorithm for your problem. Options include logistic regression, decision trees, support vector machines, and neural networks.
  • Model Training: Train your model using labelled data, select appropriate loss functions and optimization techniques, and evaluate its performance using relevant metrics.
  • Model Evaluation: Evaluate your model’s performance using metrics like accuracy, precision, recall, F1-score, and ROC-AUC, considering the trade-offs between different metrics.
  • Dealing with Imbalanced Data: Handle imbalanced datasets with techniques like oversampling, undersampling, cost-sensitive learning, or anomaly detection.

Binary classification models have wide-ranging applications, from medical diagnosis and fraud detection to sentiment analysis and recommendation systems. As you navigate the world of binary classification, keep refining your skills, experimenting with different techniques, and staying up-to-date with advancements in the field.

About the Author

Neri Van Otten

Neri Van Otten

Neri Van Otten is the founder of Spot Intelligence, a machine learning engineer with over 12 years of experience specialising in Natural Language Processing (NLP) and deep learning innovation. Dedicated to making your projects succeed.

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