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- Difference between predict vs predict_proba in scikit-learn
Please, instead of "suppose", post an actual code example of using both predict and predict_proba, so we can ground the discussion in an actual (and not hypothetical) case
- AttributeError: LinearRegression object has no attribute predict_proba
If you are in a regression setting, just replace predict_proba with predict If you are in a classification setting, you cannot use linear regression - try logistic regression instead (despite the name, it is a classification algorithm), which does indeed have a predict_proba attribute (again, see the docs)
- AttributeError: Sequential object has no attribute predict_proba
AttributeError: 'Sequential' object has no attribute 'predict_proba' Asked 3 years, 8 months ago Modified 3 years, 6 months ago Viewed 27k times
- Updating scikit-learn: SVC object has no attribute _probA?
On version 0 22, the model contained probA_ and probB_ internal attributes, but no properties _probA or _probB (as show in your case) They renamed these attributes on newer versions to _probA and _probB (as attributes, not properties) Inspecting the model before the fix, what I found was just two empty numpy arrays:
- How to get the predict_proba for the class predicted by predict in . . .
I know predict() uses predict_proba() to get the predictions, by computing the mean of the predicted class probabilities of the trees in the forest I want to get the result of predict_proba() for the class predicted by the predict() method
- Using the predict_proba () function of RandomForestClassifier in the . . .
A RandomForestClassifier is a collection of DecisionTreeClassifier 's No matter how big your training set, a decision tree simply returns: a decision One class has probability 1, the other classes have probability 0 The RandomForest simply votes among the results predict_proba() returns the number of votes for each class (each tree in the forest makes its own decision and chooses exactly
- python - How to get a classifiers confidence score for a prediction in . . .
For those estimators implementing predict_proba() method, like Justin Peel suggested, You can just use predict_proba() to produce probability on your prediction For those estimators which do not implement predict_proba() method, you can construct confidence interval by yourself using bootstrap concept (repeatedly calculate your point estimates in many sub-samples) Let me know if you need any
- AttributeError:LinearSVC object has no attribute predict_proba
You are using LinearSVC as a base for SklearnClassifier and it indeed does not have predict_proba() Please check the documentation here
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