Binary Classification in Machine Learning
Binary Classification is a supervised learning task where the goal is to predict one of two possible classes for a given input. For example, determining whether an email is “spam” or “not spam” or if a patient has a “disease” or “no disease.”
The output of a binary classification model is typically a probability, which is then converted into a class label based on a decision threshold (e.g., 0.5).
How Does Binary Classification Work?
Step 1: Data Preprocessing
The first step in binary classification involves preparing the data:
- Feature Scaling: Normalise or standardise features to ensure that all inputs contribute equally to the model.
- Handling Missing Data: Fill missing values using imputation techniques or remove incomplete records.
- Class Balancing: Address imbalanced datasets by using techniques such as oversampling, undersampling, or weighted loss functions.
- Encoding: Convert categorical variables into numerical values using techniques like one-hot encoding or label encoding.
Step 2: Choose a Model
Several algorithms can be used for binary classification, including:
- Logistic Regression: A linear model that predicts probabilities using the sigmoid function.
- Support Vector Machines (SVM): Finds the optimal hyperplane that separates the two classes.
- Decision Trees: Uses a tree structure to make decisions based on feature values.
- Random Forest: An ensemble of decision trees for more robust predictions.
- Gradient Boosting (e.g., XGBoost, LightGBM): Combines weak learners to create a strong classifier.
- Neural Networks: Deep learning models for complex datasets.
The choice of model depends on the dataset size, complexity, and computational resources.
Step 3: Train the Model
The training process involves providing labeled data to the model so it can learn the relationship between the input features and the target variable. The model minimizes a loss function, such as:
- Log-Loss (Cross-Entropy Loss): Common for probabilistic classifiers like logistic regression or neural networks.
- Hinge Loss: Used for models like SVMs.
Optimization algorithms like Gradient Descent or its variants (e.g., SGD, Adam) are used to adjust the model parameters during training.
Step 4: Make Predictions
Once trained, the model predicts probabilities for new input data points:
\( P(y=1|x) = h(x) \)
Here:
- \( P(y=1|x) \): Predicted probability that the input belongs to Class 1
- \( h(x) \): Model’s prediction function
The probability is then converted into a class label using a decision threshold (e.g., 0.5):
\( \text{Class} = \begin{cases} 1 & \text{if } P(y=1|x) \geq 0.5 \\ 0 & \text{if } P(y=1|x) < 0.5 \end{cases} \)
Key Metrics for Binary Classification
- Accuracy: The percentage of correctly predicted instances.
- Precision: The proportion of true positive predictions out of all positive predictions.
- Recall (Sensitivity): The proportion of true positives out of all actual positives.
- F1-Score: The harmonic mean of precision and recall, useful for imbalanced datasets.
- ROC-AUC: Measures the area under the Receiver Operating Characteristic curve, indicating the trade-off between true positives and false positives.
Advantages of Binary Classification
- Simplicity: Many algorithms are straightforward to implement and interpret for binary tasks.
- Efficient: Requires fewer computational resources compared to multi-class problems.
- Wide Applicability: Used in various domains such as healthcare, finance, and marketing.
Limitations of Binary Classification
- Imbalanced Classes: Performance can be biased toward the majority class if the dataset is imbalanced.
- Threshold Selection: Requires careful selection of the decision threshold to balance precision and recall.
- Restricted Scope: Limited to problems with only two possible outcomes.