I can reproduce this problem for an even more easily separable dataset:
The ideal tree for it should be as follows:
However, when I run DecisionTreeClassifier with the maximal depth = 2 in scikit-learn many times, it splits the dataset randomly and never gets it right.
This is an example of 4 different runs:
The problem is that scikit-learn has only two ...
You can use the MLP function partial_fit to perform a single training iteration at a time. If you do retrieve the weights between calls to this function, you can see what they look like after each iteration.
Here's a good resource showing you the exact code for what you're trying to do.
Here's the particular snippet:
# evaluate a model using k-fold cross-validation
def evaluate_model(dataX, dataY, n_folds=5):
scores, histories = list(), list()
# prepare cross validation
kfold = KFold(n_folds, shuffle=True, random_state=1)
# enumerate splits
The first place I would have directed you would be Sklearn and pydiffmap. I found this paper specifically about the problem you are doing using python the reference a package called megaman It seems like an active Github . I suggest not just looking at manifold learning papers but leaning towards a search toward non linear embedding or non linear ...
Yes, ML can fit a curve based on examples that include hyperparameters but not a model specification. To do this, you need to specify a family of models that is large enough to include the true model. You can then treat this as learning a relationship from 4 inputs to a single output.
For example, suppose you are willing to make only the following ...
Welcome to AI.SE Enes.
I think by random search, you are referring to so-called "black-box optimization". Random search is sometimes used as a name for this, but BBO is a more common name, and might be easier to search for.
There are many BBO techniques. 'random search' is usually used to refer to a hill-climbing algorithm where you start at a random ...