Study on Ensemble Models in Machine Learning

1. Ensemble Methods
and Support Vector
Machines (SVM) in
Machine Learning
Satvik Kundargi

2. What Are
Ensemble
Methods?
Ensemble methods combine multiple“weak”
models to create a stronger, more accurate model.
Types:
Random Forests: Builds multiple decision trees
with different subsets of data.
Gradient Boosting: Sequentially builds models,
where each corrects the errors of the previous one.

3. Random
Forests
Concept:
• Random Forests consist of multiple decision
trees trained on random subsets of data and
features.
• Final prediction is made by averaging (for
regression) or majority vote (for classification).
Key Feature: The randomness helps
reduce overfitting by lowering model
variance.

4. Gradient
Boosting
Concept:
Gradient Boosting builds models sequentially, with
each new model correcting the errors made by the
previous ones.
It optimizes performance through gradual
improvements in the error function.
Popular Variants: XGBoost, LightGBM, and
CatBoost.

5. Application of
Ensemble Methods
• Example: Customer Churn Prediction
• Scenario: A company uses ensemble
methods to predict customer churn,
identifying customers who are likely to
leave the service.
• Why Ensemble Methods?: Combining
weak models improves accuracy by
capturing diverse decision patterns,
leading to better predictions.
• Outcome: Enhanced accuracy in
identifying customers at risk of churning,
improving retention strategies.

6. Strengths and
Limitations of
Ensemble
Methods
Strengths:
Accuracy: Higher accuracy compared to individual models.
Robustness: Reduced overfitting, especially with Random
Forests.
Versatility: Can handle a variety of data types, including
structured/tabular data.
Limitations:
Complexity: Higher computational cost due to training multiple
models.
Interpretability: Less interpretable compared to simpler models
like decision trees or linear models.

7. Support
Vector
Machines
(SVM)
Definition: SVM is a machine learning
model that finds the optimal
hyperplane to separate different
classes in a dataset.
Key Idea: The hyperplane maximizes
the margin between different classes
(for classification) or fits a margin
around data points (for regression).

8. SVM for
Classification
Concept:
In classification, SVM finds the hyperplane that
separates the data into classes with the largest
possible margin.
Support Vectors: Data points that lie closest to
the hyperplane, defining the margin.
Kernel Trick: Allows SVM to handle non-linear
separations by mapping the data into higher
dimensions where it becomes linearly separable.

9. SVM for
Regression
Concept:
Instead of predicting exact values, SVM
regression fits a margin around the predicted
function with a certain tolerance level
(epsilon).
The model aims to minimize prediction error
while keeping the data points within the
margin.

10. Application of
SVM
Example: Fraud Detection
Scenario: A financial institution uses SVM to
detect fraudulent transactions based on
transaction patterns.
Why SVM?: Effective in high-dimensional spaces,
where patterns of fraud might not be linearly
separable.
Outcome: Improved accuracy in classifying
transactions as fraudulent or non-fraudulent, even
with limited training data.

11. Strengths and
Limitations of
SVM
Strengths:
High-Dimensional Data: SVM performs well when there are more
features than samples.
Kernel Trick: Makes SVM adaptable to different types of data
distributions (linear or non-linear).
Robustness: Works well even with complex, non-linearly
separable data.
Limitations:
Large Datasets: SVM becomes computationally expensive as the
dataset size increases.
Parameter Tuning: Requires careful tuning of hyperparameters
(e.g., kernel, C, gamma) for optimal performance.

12. Key
Takeaways
Ensemble Methods:
• Combine multiple models (Random
Forests, Gradient Boosting) to improve
prediction accuracy and reduce overfitting.
• Effective for applications like customer
churn prediction.
SVM:
• Finds the optimal boundary (hyperplane) for
classification and regression tasks.
• Effective in high-dimensional data but
sensitive to large datasets.