I am trying to find the most valuable features by applying feature selection methods to my dataset. Im using the SelectKBest function for now. I can generate the score values and sort them as I want, but I don’t understand exactly how this score value is calculated. I know that theoretically high score is more valuable, but I need a
Tag: scikit-learn
Imbalanced-Learn’s FunctionSampler throws ValueError
I want to use the class FunctionSampler from imblearn to create my own custom class for resampling my dataset. I have a one-dimensional feature Series containing paths for each subject and a label Series containing the labels for each subject. Both come from a pd.DataFrame. I know that I have to reshape the feature array first since it is one-dimensional.
Why does this decision tree’s values at each step not sum to the number of samples?
I’m reading about decision trees and bagging classifiers, and I’m trying to show the first decision tree that is used in the bagging classifier. I’m confused about the output. Here’s a snippet out of the output It’s been my understanding that the value is supposed to show how many of the samples are classified as each category. In that case,
Macro VS Micro VS Weighted VS Samples F1 Score
In sklearn.metrics.f1_score, the f1 score has a parameter called “average”. What does macro, micro, weighted, and samples mean? Please elaborate, because in the documentation, it was not explained properly. Or simply answer the following: Why is “samples” best parameter for multilabel classification? Why is micro best for an imbalanced dataset? what’s the difference between weighted and macro? Answer The question
module ‘numpy’ has no attribute ‘dtype’
When importing sklearn datasets eg. I get the error I am not sure why I get this I don’t get this error when running things from a jupyter notebook, which is also weird. Any help on this issue would be greatly appreciated Answer I figured this out. The answer is that the file I was running was named numbers.py. This
How to use pickle to save sklearn model
I want to dump and load my Sklearn trained model using Pickle. How to do that? Answer Save: Load: In the specific case of scikit-learn, it may be better to use joblib’s replacement of pickle (dump & load), which is more efficient on objects that carry large numpy arrays internally as is often the case for fitted scikit-learn estimators: Save:
TypeError: train_test_split() got an unexpected keyword argument ‘test_size’
I’m trying to find the best feature set using random forest approach I need to split the dataset into test and train. here is my code parameters data,data_y are parsing correctly. But I’m getting the following error. I couldn’t figure out why this is. Answer You are using the same function name in your code same as the one from
confusion matrix error “Classification metrics can’t handle a mix of multilabel-indicator and multiclass targets”
I am getting a error when I try to use confusion matrix. I am doing my first deep learning project. I am new to it. I am using the mnist dataset provided by keras. I have trained and tested my model successfully. However, when I try to use the scikit learn confusion matrix I get the error stated above. I
Why do I get this import error when I have the required DLLs?
getting this error Answer According to this github issue https://github.com/hmmlearn/hmmlearn/issues/87 “The solution is to install mkl.” General advice in case like this is to google last two lines of the stack trace, usually you will find a github or similar thread about it.
Load Machine Learning sklearn models (RandomForestClassifier) through java and send as argument to a function in python file
I have a ML model which is trained as saved as pickle file, Randomforestclassifier.pkl. I want to load this one time using java and then execute my “prediction” part code which is written python. So my workflow is like: Read Randomforestclassifier.pkl file (one time) Send this model as input to function defined in “python_file.py” which is executed from java for