Data Science Cheat Sheet

Models

• Support Vector Machine (SVM): A supervised model that finds the optimal hyperplane for class separation, widely used in high-dimensional tasks like text classification (e.g., spam detection).
  • Advantage: Effective in high-dimensional spaces and robust to overfitting with the proper kernel.
  • Disadvantage: Computationally intensive on large datasets and sensitive to parameter tuning.
• k-Nearest Neighbors (kNN): A non-parametric method that classifies based on nearest neighbors, often applied in recommendation systems and image recognition.
  • Advantage: Simple and intuitive, with no training phase, making it easy to implement.
  • Disadvantage: Computationally expensive at prediction time, especially with large datasets, and sensitive to irrelevant features.
• Decision Tree: A model that splits data into branches based on feature values, useful for interpretable applications like customer segmentation and medical diagnosis.
  • Advantage: Highly interpretable and handles both numerical and categorical data well.
  • Disadvantage: Prone to overfitting, especially with deep trees, and can be sensitive to small data changes.
• Linear Regression: A statistical technique that predicts a continuous outcome based on linear relationships, commonly used in financial forecasting and trend analysis.
  • Advantage: Simple and interpretable, with fast training for large datasets.
  • Disadvantage: Assumes a linear relationship, so it's unsuitable for complex, non-linear data.
• Logistic Regression: A classification model estimating the probability of a binary outcome, widely used in credit scoring and binary medical diagnostics.
  • Advantage: Interpretable with a clear probabilistic output, efficient for binary classification.
  • Disadvantage: Limited to linear boundaries, making it ineffective for complex relationships without transformations.
• Naive Bayes: A probabilistic classifier assuming feature independence, effective in text classification tasks like spam filtering due to its speed and simplicity.
  • Advantage: Fast and efficient, especially on large datasets with independence assumptions holding.
  • Disadvantage: Assumes feature independence, which may reduce accuracy if dependencies exist between features.

Confusion Matrix and F1 Score

Confusion Matrix

	Predicted Positive	Predicted Negative
Actual Positive	True Positive (TP)	False Negative (FN)
Actual Negative	False Positive (FP)	True Negative (TN)

F1 Score = 2 * (Precision * Recall) / (Precision + Recall)

• 2 * (Positive Predictive Value * True Positive Rate) / (Positive Predictive Value + True Positive Rate)
• 2 * (TP) / (TP + FP + FN)

Key Evaluation Metrics

True Positive Rate (TPR), Sensitivity, Recall

• TPR = Sensitivity = Recall = TP / (TP + FN)
• Application: Measures the model's ability to correctly identify positive cases, useful in medical diagnostics to ensure true positives are detected.

Precision (Positive Predictive Value)

• Precision = TP / (TP + FP)
• Application: Indicates the proportion of positive predictions that are correct, valuable in applications like spam filtering to minimize false alarms.

Specificity (True Negative Rate, TNR)

• Specificity = TNR = TN / (TN + FP)
• Application: Assesses the model's accuracy in identifying negative cases, crucial in fraud detection to avoid unnecessary scrutiny of legitimate transactions.

False Positive Rate (FPR)

• FPR = FP / (FP + TN)
• Application: Reflects the rate of false alarms for negative cases, significant in security systems where false positives can lead to excessive interventions.

Negative Predictive Value (NPV)

• NPV = TN / (TN + FN)
• Application: Shows the likelihood that a negative prediction is accurate, important in screening tests to reassure negative cases reliably.

Accuracy

• Accuracy = (TP + TN) / (TP + TN + FP + FN)
• Application: Provides an overall measure of model correctness, often used as a baseline metric but less informative for imbalanced datasets.

Curves & Chart

Lift Curve

• X-axis: Percent of data (typically population percentile or cumulative population)
• Y-axis: Lift (ratio of model's performance vs. baseline)
• Application: Helps in evaluating the effectiveness of a model in prioritizing high-response cases, often used in marketing to identify segments likely to respond to promotions.

Gain Chart

• X-axis: Percent of data (typically cumulative population)
• Y-axis: Cumulative gain (proportion of positives captured)
• Application: Illustrates the cumulative capture of positive responses at different cutoffs, useful in customer targeting to assess the efficiency of resource allocation.

Cumulative Response Curve

• X-axis: Percent of data (cumulative population)
• Y-axis: Cumulative response (actual positives captured as cumulative total)
• Application: Evaluates model performance by showing how many true positives are captured as more of the population is included, applicable in direct marketing to optimize campaign reach.

ROC Curve

• X-axis: False Positive Rate (FPR)
• Y-axis: True Positive Rate (TPR or Sensitivity)
• Application: Used to evaluate the trade-off between true positive and false positive rates at various thresholds, crucial in medical testing to balance sensitivity and specificity.

Precision-Recall Curve

• X-axis: Recall (True Positive Rate)
• Y-axis: Precision (Positive Predictive Value)
• Application: Focuses on the balance between recall and precision, especially useful in cases of class imbalance, like fraud detection or medical diagnosis, where positive class accuracy is vital.