Fuck you

M1

• PyCaret can underperform if sample is way too unbalanced
• If duplicates take too much row, it can make model dumb
• Types of learning
  • Supervised learning:
  • classification & regression models
  • Unsupervised learning:
  • k-means, PCA
  • Reinforcement learning
  • deep learning models
• AI winters:
  • 1st: mid 1970s, too weak and think too much
  • 2nd: 1980-1990s, same fucking thing, got better due to advancements and govt funds
• AI singularity: AI > human, time is uncertain

M2

• preprocess
  • Preprocess because result might skew a lot if not preprocessed
  • nans can be removed by doing nothing, filling with a specific value, or impute using knn (get number by considering similar points)
• encodings
  • label encoding is needed when cat number is high, but might introduce non-existent hierarchy in labels
  • dummy encoding can create multicollinearlity and could cause model to overfit
• scaling
  • Whether scale (StandardScaler) or not:
  • Scale: distance based methods (SVM, KNN), because magnitude mean serious shits
  • NOT to scale: trees or need to be intrepreted
  • whether normalize (MinMaxScaler) or not:
  • when value need to be in a specific range
  • not gaussian
• sampling
  • Oversampling is to make up new data points for minority class
  • Undersampling is to cut data points from majority class
• feature engineering
  • RFE is used to eliminate unnecessary features
  • cluster label can be used as a feature
• metrics
  • Accuracy can be misleading due to class imbalance
  • Precision does not consider false negatives
  • Recall does not consider false positives
  • F1 cannot fit uneven importance of recall & precision
  • ROC curve and Area Under the ROC Curve cannot explain model at specific points

M3

• metrics
  • why care about it
  • model eval & compare & optimization
  • understaning model behaviour
  • align with business, aid decision making
  • types: MSE, RMSE, MAE, MAPE, R2
  • how to choose:
  • MSE can be influenced by outliers
  • RMSE is good when large error is more fucked up
• bias is being too naive, variance is being too bitchy
• model fit
  • underfitting -> make model more complex
  • overfitting -> regularization, to make model dumber by adding penalty term relalted to modle complexity
  • L1: add abs of weights to loss function
  • L2: add squared value of weights to loss function
  • heterosca…city is the concept that variance of the model is changing
  • multicollineatity is when 2 predictors are highly correlated. Debug by finding all correlations and eliminate
• Ensembles
  • bagging: train multiple identical models by using subsets of original training set (drawn with replacement) and vote to get output
  • boosting: subsequent models try to correct error from the prev one
  • stacking: train multiple different models and train another fucking model to model those fucking models
• NFL is saying one size cant fit all

M4

• Curves
  • ROC curve, y=TP, x=FP, twerking towards top left is considered good
  • The area if above 0.8 is considered good
• propensity: the likelihood that a given instance belongs to a particular class
• errors:
  • FP = type I error
  • FN = type II error
  • cost sensitive classification does not treat these 2 errors equally
  • can implement by assigning class weights
• Rashomon set says that a set of different mother fucking models can perform equally good on the same task
• LightGBM vs XGBoost:
  • LGBM grows tree on leaf wise, thus
  • faster in training
  • less resource usage
  • more sensitive to hyperparam
  • XGB grows tree on level wise

M5

• themes of RAI:
  • transparency and explanability of model itself
  • fairness and bias mitigation of model on real life cases
  • safeguard data from users
  • help human desicion making
  • create social impact, and create accountability on social issues
• Interpreability vs. explainability
  • explanability: model’s transformation of data is easy to understand
  • interpretability: how the model made that fucking transformation, algorithm wise, is easy to understand
• Global explanation
  • PDP: how the change in one feature inpact the output, keeping others constant
  • SHAP: shows the contribution of each feature of difference btwn model’s prediction and average prediction across all points
  • Surrogate models: mimic black box while being more explanable
• Local explanation
  • Local Interpretable Model-agnostic Explanations: approximate the model locally using a more explainable model, shows why a row is classified in a certain way, and how each feature contributed to that prediction
  • debugging and model validation
  • SHAP
  • high stake ops
  • Counterfactural explanations: change value to one feature and see if the result changes
  • understanding decision boundaries
  • Anchors: find a set of features that guarantee the same prediction when satisfied regardless of all other things
  • explaining individual predictions for tree based models
• RAI challenges
  • engage w/ stakeholders, s.t.
  • ensuring fairness
  • addressing social implications
  • implementing mechanisms for transparancy
  • establish guidelines

M6

• instrumental vars: a var that has no absolutely shit on the final decision of y, like your mom on the your decision of being a bitch
• causal inference: cannot observethe counterfactual outcome
• confounders: things that have effect on treatment assignment and outcome
  • output might have effect on treatment
  • time-varying
  • experiments need to be designed like in fucking biology to mitigate it
• challenges of causal ML
  • time & space complexity
  • cannot completely set apart btwn units
  • ensuring intrepretability and transparency
• correlation != causality
• Double ML how:
  • control group vs. exp group

M7