I was following a tutorial which was available at Part 1 & Part 2. Unfortunately the author didn’t have the time for the final section which involved using cosine similarity to actually find the distance between two documents. I followed the examples in the article with the help of the following link from stackoverflow, included is the code mentioned in the above link (just so as to make life easier)
I am currently using H2O for a classification problem dataset. I am testing it out with H2ORandomForestEstimator in a python 3.6 environment. I noticed the results of the predict method was giving values between 0 to 1(I am assuming this is the probability).
How can I plot a dendrogram right on top of a matrix of values, reordered appropriately to reflect the clustering, in Python? An example is the following figure:
I have Keras installed with the Tensorflow backend and CUDA. I’d like to sometimes on demand force Keras to use CPU. Can this be done without say installing a separate CPU-only Tensorflow in a virtual environment? If so how? If the backend were Theano, the flags could be set, but I have not heard of Tensorflow flags accessible via Keras.
I apply the
decision tree classifier and the random forest classifier to my data with the following code:
I’m using Python and have some confusion matrixes. I’d like to calculate precisions and recalls and f-measure by confusion matrixes in multiclass classification. My result logs don’t contain y_true and y_pred, just contain confusion matrix. Could you tell me how to get these scores from confusion matrix in multiclass classification? Answers: Thank you for visiting … Read more
I’m trying to train a network with an unbalanced data. I have A (198 samples), B (436 samples), C (710 samples), D (272 samples) and I have read about the “weighted_cross_entropy_with_logits” but all the examples I found are for binary classification so I’m not very confident in how to set those weights.
Let’s suppose I have a sequence of integers:
I have performed a PCA analysis over my original dataset and from the compressed dataset transformed by the PCA I have also selected the number of PC I want to keep (they explain almost the 94% of the variance). Now I am struggling with the identification of the original features that are important in the reduced dataset.
How do I find out which feature is important and which is not among the remaining Principal Components after the dimension reduction?
Here is my code: