MM - Explain Model building phase with its challenges.

Creating a mind map to facilitate future recall of the model building phase in data science can be highly effective. Here are some keywords and short phrases you might consider using to structure your mind map:

Model Building Phase

• Data Selection
  • Relevant variables
  • Data filtering
• Preprocessing
  • Missing values
  • Normalize/Standardize
  • Encode categories
  • Dimensionality reduction
• Modeling Technique
  • Classification
  • Regression
  • Clustering
• Data Splitting
  • Training set
  • Testing set
• Training
  • Fit model
  • Parameter adjustment
• Evaluation
  • Accuracy, Precision, Recall, F1
  • MSE, RMSE
• Parameter Tuning
  • Grid search
  • Random search
• Validation
  • Cross-validation
  • Performance on new data

Challenges

• Overfitting vs. Underfitting
  • Too complex vs. too simple
• Data Quality
  • Accuracy
  • Relevance
• Model Complexity
  • Appropriate complexity
  • Computational demands
• Scalability
  • Handling large data
  • Efficient computation
• Bias-Variance Tradeoff
  • Bias errors
  • Variance errors
• Ethical/Legal Issues
  • Privacy
  • Bias in predictions

These keywords and short phrases should help you construct a comprehensive and easy-to-navigate mind map, which will aid in future recollections and deeper understanding of the model building phase in data science.