Solved – Can we make the machine learn Gates (OR AND XOR etc.)

Below is the NAND Gate truth table, there are 2 independent features A,B and one dependent feature Y in the dataset.

Can we make the machine learn this if YES how ? if No why ?

Please go through the attempt below where the classifier model stumble on [0,0] point and can't predict it correctly giving .75 accuracy.

``import pandas as pd  from sklearn.linear_model import LogisticRegression from sklearn.metrics import confusion_matrix,classification_report,accuracy_score  x_train = [[0,0],[0,1],[1,0],[1,1]] y_train = [1,1,1,0]  x_test = [[1,1],[1,0],[0,1],[0,0]]  clf_lr = LogisticRegression() clf_lr.fit(x_train,y_train) prediction = clf_lr.predict(x_test)  print(prediction)  [1 1 1 1]  print(accuracy_score(y_train,prediction)) 0.75  print(confusion_matrix(y_train,prediction))  [[0 1]  [0 3]]  print(classification_report(y_train,prediction))                 precision    recall  f1-score   support            0       0.00      0.00      0.00         1           1       0.75      1.00      0.86         3  avg / total       0.56      0.75      0.64         4 ``
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But your example is the NAND gate, which is linearly separable, so a logistic regression should be able to learn it. The problem is that `sklearn` applies L2 regularization by default, which is preventing the model from learning the pattern "strongly" enough. You can see from `clf_lr.predict_proba(x_test)[:, 1]` that the model has learned that $$(1,1)$$ is less likely to be a 1, but the regularization has prevented the probability from dropping below $$0.5$$. You can reduce the regularization strength, or better set `penalty='none'`, to recover correct predictions.