Logistic Regression doesnt learn anything
I train binary LR in python (opencv 3.1 and 4.1) and get always prediction = 1. When I try it on sklearn it works. OpenCV DT and ANN work too.
I tried to train LR with different parametres. Actual version:
dtype = np.float32
train_data = cv.ml.TrainData_create(train_features, cv.ml.ROW_SAMPLE, train_labels.astype(dtype))
cv_model = cv.ml.LogisticRegression_create()
cv_model.setLearningRate(5e-5)
cv_model.setRegularization(cv.ml.LogisticRegression_REG_L2)
cv_model.setTrainMethod(cv.ml.LogisticRegression_MINI_BATCH)
cv_model.setMiniBatchSize(128)
cv_model.setTermCriteria((cv.TERM_CRITERIA_COUNT, int(1e+7), 1e-7))
cv_model.train(train_data)
Did you normalized your data ?
No I didnt. Is it necessary in this case?
what is
train_data.size()?train_features.shape ((448, 4) train_labels.shape (448,))
So you have 4 features with 448 data rows in total? Can you also post the code where you do your prediction(where it goes wrong)? I am no expert - at least the training code looks good to me (i dont know the range of your feature values and if the need to be normalized at all - i could imagine sklearn does this for you - only a wild guess).
I tried with normalization and got the same result:
I make prediction by this:
https://github.com/opencv/opencv/blob...
(i guess, your learningRate is too low)
Good point berak - But with a low value it just takes long to converge. How about picking another term criteria - instead of count use accuracy? I just checked my yolo config - it has learning rate of 0.0005