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I have a pandas dataframe df, and I want to show the histogram. df.hist(bins=100, label="myhist") Now, for some reason I have lots of zeros in this df, so I only want to show the values between 1 a …

Ok, after some digging around I found that I can pass a range = (1,100) and that does the trick.

I am new to ML and I created a super basic logistic regression example with 4 points on the $x$ line that belong to two classes: points = [[1, 1]] points = points + [[2, 0]] points = points + [[1.5, …

I am looking at this tutorial: https://www.dataquest.io/mission/75/improving-your-submission At section 8, finding the best features, it shows the following code. import numpy as np from sklearn.f …

I am trying to understand some simple neural net case using theano. The deeplearning.net site gives the following simple code for implementing a logistic regression application to a simple case: impo …

Found the issue, a simple bug in my code. I wrote: for index in result: if result[index] != 0: error += 1 while it should have been something like: for index in range(N): if result …

I am trying to create a large model. One of the features is categorical, and it has almost 100 million entries. I have looked at sklearn LabelEncoder, but I am concerned that it will still create an …

Kmeans clustering will randomly select the initialisation points and then run the algorithm until convergence is reached. Is there a way I can choose my own initialisation points and pass them into th …

I wrote a very simple and compact logistic regression program using theano. I am initialising my data randomly, and I have restricted the number of training steps to 1, so since the weights are also r …

I know there is a partial answer here but my question is slightly different. I have implemented a decision tree in sklearn. Say I have $2^n$ different values for a feature, with just one feature. I wa …

Can I set up sklearn in python to do this? How? …

I am using a standard linear regression using scikit-learn in python. However, I would like to force the weights to be all non-negative for every feature. is there any way I can accomplish that? …

I have a dataset and need to predict, out of 9 variables, which ones matter most to predict number 10. I first tried using the selectKBestmethod from sklearn.feature_selection and it looks like 4,5,6 …

I have a set of 10,000 integers, and another set of 100. The integers in the first set are mapped to integers in the second set according to some rules (not mathematical rules, think of these values a …

I have a set of features, one of which is a string. I convert the string to an integer by treating the string as a base 36 number (I only use the first 13 characters). Then I can use DecisionTrees sin …