Activities - Assess Information Gain and Conclude

#	Name	Abbrev.	Guidance	Actions
Explore Data and Find Signal 5
1	Interpret correlation with p-value and rule out spurious relationships	ACE-1	Cross each correlation's sign/magnitude against its p-value (p≤.01 vs not) before treating it as evidence. …	Read the 'ACE-1 Interpret correlation with p-value and rule out spurious relationships' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Assess Information Gain and Conclude'). Follow its guidance when assisting me. Cross each correlation's sign/magnitude against its p-value (p≤.01 vs not) before treating it as evidence. Remember correlation is not causation — a real correlation may share a hidden confounder — and a non-significant linear correlation doesn't rule out a non-linear relationship.
2	Design and run a predictive experiment	ACE-2	Split the feature table 70:30 train/test (seed the RNG first). Choose the model by Y's …	Read the 'ACE-2 Design and run a predictive experiment' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Assess Information Gain and Conclude'). Follow its guidance when assisting me. Split the feature table 70:30 train/test (seed the RNG first). Choose the model by Y's type (per the Target Variable (Y) Definition from EFS): continuous Y → OLS `LinearRegression`, or `Ridge` if predictors are collinear; categorical/binary Y → `LogisticRegression` (use `cut()` first to quantize a continuous proxy into labels if needed, and set `C` well above the default 1.0, e.g. ~20). Fit on training data only, engineering nonlinear predictors (squares/logs/interaction terms) first if a plain linear fit under-captures curvature, then apply the fitted model to the held-out test set. Never touch the test data while fitting.
3	Evaluate model quality	ACE-3	Build a confusion matrix (classification) or compare predicted-vs-actual on train and test sets (regression); derive …	Read the 'ACE-3 Evaluate model quality' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Assess Information Gain and Conclude'). Follow its guidance when assisting me. Build a confusion matrix (classification) or compare predicted-vs-actual on train and test sets (regression); derive accuracy/precision/sensitivity/specificity and choose which one matters for the question at hand. Compare train-set fit against test-set fit to judge overfitting before trusting the model.
4	Use clustering as an alternative when correlation is weak	ACE-4	When linear correlation is weak or absent but you suspect structure, scale the features (sklearn.preprocessing.scale()), …	Read the 'ACE-4 Use clustering as an alternative when correlation is weak' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Assess Information Gain and Conclude'). Follow its guidance when assisting me. When linear correlation is weak or absent but you suspect structure, scale the features (`sklearn.preprocessing.scale()`), run k-means (`KMeans(n_clusters=k)`, k chosen by judgment), and assign human-meaningful labels to the resulting clusters — cluster IDs alone mean nothing.
5	Rank features by information gain and draw conclusions	ACE-5	Fit a Random Forest Regressor on the feature table against Y to get a feature-importance …	Read the 'ACE-5 Rank features by information gain and draw conclusions' activity from playbook 'EDA with Python, Pandas and Jupyter' (workflow 'Assess Information Gain and Conclude'). Follow its guidance when assisting me. Fit a Random Forest Regressor on the feature table against Y to get a feature-importance ranking (an ensemble-of-trees estimate of how much each feature actually informs Y), cross-check it against the regression coefficients and correlations from earlier steps, and write the surviving, validated findings as the phase's Conclusions.

Activities in Assess Information Gain and Conclude