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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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The dataset I have has the genders as M and F. I want to change this to numbers. So I was wondering, which would be better for machine learning? Doing a .replace() or use .get_dummies for values like this.
Lets say if I had a dataset that labeled the gender as M and F and I wanted to change this categorical value to numeric ones, which would be better for machine learning? Doing a .replace() or using .get_dummies.
I'm new to this and just wondering which would be better.
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I have one data frame in python. How can I calculate the 3sigma value for each column of my data frame using python? please help me with this.
The command you're looking for is df.std() * 3
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I need a trained deep learning mode can compare two images for two persons and give me the result as if the two image are for the same person or not.
I guess you see the face of the two persons on the image. In this case you can try to use Facenet Paper
You can find an implementation for example here link
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Im trying to find a way to compare what students performed consistantly in their InternalAssessment_Performance to their FinalExam_Performance. Essentially i need to find what students have the same answer in both those columns.
How is it possible to compare the values in both commons and have them returned if they are the same?
Any help no matter how small would be great.
If the columns are aligned you can do something like this:
df[df['InternalAssessment_Performance'] == df['FinalExam_Performance']]
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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)