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Would someone please kindly explain what sklearn.tree.DecisionTreeClassifier.predict(X) and .predict_log_proba(X) and .predict_proba(X) are?
Thanks a lot in advance.
Here's the link to sklearn's library:
In short words (and this applies to all sklearn models):
predict_proba(x) = P(y|x) (probability of each label as a vector)
predict_log_proba(x) = log P(y|x) (logarithm of the above)
predict(x) = arg max_y P(y|x) (the most probable label using the above)
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I have following algorithm I will implement in Python:
I'm not sure how to build it up and especially to deal with the minimum function. Can anyone help me?
I have self made the norm of the gradiant calculated out from matrix A and vector b by following:
r=b-A#x
r_new=np.inner(r,r)
np.sqrt(r_new)
But how do I deal with the minimum function and the setup. Can anyone help me?
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If I have a binary classification problem that I want to build a model from using sklearn or matlab. Should the label files contain 0 and 1 or it can instead contain the name for classes for example "R" (for rainy) and "S"(for sunny)? should I convert it to 0 and 1?
The type of label should have no influence on the model. Regardless of whether you use 0 and 1 or R and S you should get the same exact results.
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I have a question about the Levenberg-Marquardt optimize method in Python:
Generally, the Lavenberg Maquardt is used for deterministic systems. Can I use it for stochastic model to estimate unknown parameters (inputs of my model).
Thanks
The requirement for the Levenberg Marquard algorithm is that you need to be able to calculate the jacoboan (derivative with respect to your parameter).
If this is the case for your problem then yes. I guess that it is not.
Perhaps the simplex algorithm is what you are looking for.
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Is it mean square error? The documentation doesn't give much detail.
By default, the score computed at each CV iteration is the score method of the estimator.
In other words, it does whatever the score method of your model does (or calls the provided scoring function); cross_val_score is just responsible for doing the cross-validation, not for defining what a "score" actually is.
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I am trying to find top 100/1000 words based on tfidfVectorizer output of Python's scikit-learn library. Is there a way to do it using a function from the scikit libraries?
Thanks for help
What do you mean by top 100/1000 words? The most frequent words in a dataset? You can use the Counter class of the Python standard library to do that. No need for scikit-learn.