Adding some notes

notes/20250606_minutes.txt

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+Date: 8.june 2025
+
+Meeting with Zahra
+
+Dates:
+  30.6. - Zahra has presentation
+  5.7.  - Zahra visits Iran for 2-3 weeks
+
+Asking about accuracy measures:
+  * for fit we have goodness of fit measures:
+    - log likelihood
+    - AIC and BIC
+  * these models used for classification: [1]
+  -  pred Y = [ Y : f(x|Y) > 0.5 === decision function(x) > 0
+              [ !Y : otherwise
+  - in logit models: [2]  Speech and Language Processing, Ch 5
+      decision_function(x) = b + w^T x == logit (x)
+      logit == log odds
+      logit(x) = 1/(1 + exp(-decision_function(x))
+  - ACC == accuracy score [3]
+      accuracy(y, \hat{y}) = 1/n \sum_{i=0}^{n-1} delta_{\hat{y}_i, y_i}
+
+  - AUC == area under curve ???? [4,5]
+      From [6]:
+
+        fpr, tpr, thresholds = ROC = function(y_true, y_score)
+
+        We are systematically doing
+
+          pred Y[i] = [ 1 : y_score[i] >= threshold
+                     [ 0 : otherwise
+          all i
+
+        and comparing with
+
+          y_true[i] all i
+        for all meaningful thresholds == y_score
+
+      Target scores, can either be probability estimates of the positive class,
+      confidence values, or non-thresholded measure of decisions (as returned by
+      “decision_function” on some classifiers). For decision_function scores,
+      values greater than or equal to zero should indicate the positive class.
+      In our case y_score = prob estimate(for Y=1) as given by the model
+
+
+Ref:
+   - [1] https://realpython.com/logistic-regression-python/
+   - [2] https://web.stanford.edu/~jurafsky/slp3/5.pdf
+   - https://en.wikipedia.org/wiki/Logistic_regression
+
+   - [3] https://scikit-learn.org/stable/modules/model_evaluation.html#accuracy-score
+   - [4] https://scikit-learn.org/stable/modules/model_evaluation.html#roc-metrics
+   - [5] https://en.wikipedia.org/wiki/Receiver_operating_characteristic
+   - [6] https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html
+
+   - https://stats.stackexchange.com/questions/354709/sklearn-metrics-accuracy-score-vs-logisticregression-score
+   - https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html
+   - https://scikit-learn.org/stable/modules/model_evaluation.html#roc-metrics
+
+Plan:
+
+  * Make notes overleaf file (ZA)
+    - notes will the basis for the paper and presentations
+  * Transfer as much text and results to notes (ZA)
+  * Test calc. with bayesian model (MH)
+  * Chat about progress at the picnic
+
+Ideal:
+  * Have some preliminary results for bayesian model until Friday
+
+