Model Comparison - Linear, Logistic, NN, KNN, etc
To understand the models, I prefer to compare compute. consumption(runtime), accuracy, etc.
This is not indepth introduction, but rather sharing an approach to compare models quickly.
Know your data, know your models, focus on the basics.
Chapters:
0:00 Forewords
0:33 Summary of the project
6:28 Packages of the code and refining of the code
9:05 Data preparation
10:00 Correlation check
10:42 Training of the models
13:46 visualization of the results, metrics(runtime, accuracy, mse)
16:08 3D visual
16:34 Plotly for visual improvements on metrics bar charts
17:59 Plotly for visual improvements on 3D metrics grid
19:05 GPT version complexity summary
19:49 Ploty for 3D metrics grid, focusing on parameters to show complexity
22:05 Overfitting and possible solutions
Summary:
• 🔍 Introduction to Model Comparison: The video explains the basics of statistical and supervised learning, emphasizing their practical uses in data analysis, prediction, and classification.
• 💾 Dataset Overview: The presenter uses the credit card default dataset from KLE, highlighting the importance of understanding the data before model comparison.
• 📊 Standardization and Preparation: Steps for standardizing data, separating numerical and categorical variables, and converting categorical variables to dummy variables.
• ⏱️ Runtime and Complexity: Comparison of models based on runtime, complexity, and interpretability, mentioning linear regression, logistic regression, neural networks, and more.
• 📈 Overfitting and Model Performance: Discussion on overfitting, its detection, and solutions such as cross-validation, regularization, and hyperparameter tuning.
• 🔄 Iterative Code Refinement: The iterative process of refining code with large language models like ChatGPT, focusing on transforming numerical data and handling categorical variables.
• 📉 Visualizing Results: Visualization techniques using 2D and 3D plots, and tools like Plotly to compare model performance metrics such as accuracy and mean squared error.
• 🧩 Clustering and 3D Visualization: Explanation of clustering models and 3D visualization to better understand model performance and relationships.
Insights Based on Numbers:
• Model Runtime: Neural networks have the highest runtime due to their complexity and large parameter sets, making them less suitable for local machines compared to simpler models like linear regression.
• Overfitting Detection: Overfitting is identified when training accuracy is significantly higher than test accuracy, indicating that the model performs well on training data but poorly on unseen data.
• Accuracy and Parameters: There is no significant correlation between the number of parameters and test accuracy, with simpler models often achieving comparable accuracy to more complex models like neural networks
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