Example ppt | Machine learinig | Hofstra University

Support Vector Machine (SVM)

Support Vector Machine (SVM) model tailored for binary classification in Dataset 1.

Balanced class weights incorporated during training to address imbalanced classes within the dataset.

Linear kernel suitable when the relationship between features and target variable is approximately linear.

Collective use of linear kernel and balanced class weights aims for a balance between model complexity and interpretability.

Collective use of linear kernel and balanced class weights aims for a balance between model complexity and interpretability.

Balanced class weights ensure equal consideration for instances from both classes, preventing bias toward the majority class.

Overall effectiveness in handling diverse datasets emphasized.

Overall effectiveness in handling diverse datasets emphasized.

Overall effectiveness in handling diverse datasets emphasized.

Overall effectiveness in handling diverse datasets emphasized.

KNN

For Dataset 1:

KNN model with distance-based weights.

Features either scaled or unscaled based on KNN preprocessing needs.

Learns patterns in training, assigns higher weights to closer neighbors.

For Dataset 2:

KNN model configured with 10 neighbors.

Balances model complexity and stability.

More neighbors (10) for robustness to noise and outliers.

Promotes stability and generalization in scenarios where intricate patterns are not essential.

Perceptron

Perceptron used for both datasets with default settings.

Default configuration suitable for linearly separable datasets.

Balanced class weights address class imbalance during training.

Perceptron’s simplicity and interpretability make it effective for binary classification.

Training and evaluation include confusion matrix and classification report for performance insights.

Classification Report

In Dataset 1 with class imbalance, F1-Score prioritized for balanced model assessment.

F1-Score’s balance between precision and recall suits imbalanced datasets, providing a comprehensive measure.

Dataset 2, with null values, highlights the importance of handling missing data.

F1-Score crucial in maintaining balance between precision and recall, especially when missing data impacts accuracy.

F1-Score

Results

Conclusion and Analysis of Result

Dataset 1:

Limited F1-Score improvement with oversampling and cross-validation.

Class imbalance may benefit from adjusting weights, different sampling methods, or advanced algorithms.

Hyperparameter tuning and comprehensive feature engineering could enhance predictive accuracy.

Manual data import for the minority class may improve models.

Dataset 2:

Models perform well, but 40% of features are computationally generated.

Focus on addressing null values is crucial for refining predictions.

Techniques like imputation and models resilient to missing data (e.g., decision tree-based) may help.

Feature engineering and thorough data cleaning processes could enhance robustness and accuracy.

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