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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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On geeksforgeeks in Selection sort algorithms it says that "It can cause poor branch prediction due to its high branch misprediction rate" and "Selection sort algorithm needs to iterate over the list multiple times, thus it can lead to an unbalanced branch" as disadvantage. What does it mean by branch or unbalanced branch or poor branch prediction?
I am expecting expaination for the concept of branch in any programming langauge.
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I am trying to understand what is Brute Force approach in Python?
I have a Lennard-Jones potential equation and my interatomic distance value is unknown, so my professor told me to use the Brute Force approach.
I don't know how to do so, can you explain it to me with a little simple code example?
Thank you
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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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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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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)