I want to implement a machine learning algorithm in scikit learn, but I don’t understand what this parameter random_state does? Why should I use it? I also could not understand what is a Pseudo-random number. 7 Answers 7
Can I extract the underlying decision-rules (or ‘decision paths’) from a trained tree in a decision tree as a textual list? Something like: if A>0.4 then if B<0.2 then if C>0.8 then class=”X” 23 Answers 23
I need to fit RandomForestRegressor from sklearn.ensemble. forest = ensemble.RandomForestRegressor(**RF_tuned_parameters) model = forest.fit(train_fold, train_y) yhat = model.predict(test_fold) This code always worked until I made some preprocessing of data (train_y). The error message says: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example … Read more
I have a pandas dataframe with mixed type columns, and I’d like to apply sklearn’s min_max_scaler to some of the columns. Ideally, I’d like to do these transformations in place, but haven’t figured out a way to do that yet. I’ve written the following code that works: import pandas as pd import numpy as np … Read more
Is it possible to specify your own distance function using scikit-learn K-Means Clustering? 8 Answers 8
How can I find the p-value (significance) of each coefficient? lm = sklearn.linear_model.LinearRegression() lm.fit(x,y) 9 Answers 9
I know I could implement a root mean squared error function like this: def rmse(predictions, targets): return np.sqrt(((predictions – targets) ** 2).mean()) What I’m looking for if this rmse function is implemented in a library somewhere, perhaps in scipy or scikit-learn? 14 Answers 14
I have a pandas dataframe and I wish to divide it to 3 separate sets. I know that using train_test_split from sklearn.cross_validation, one can divide the data in two sets (train and test). However, I couldn’t find any solution about splitting the data into three sets. Preferably, I’d like to have the indices of the … Read more
I am using sklearn and having a problem with the affinity propagation. I have built an input matrix and I keep getting the following error. ValueError: Input contains NaN, infinity or a value too large for dtype(‘float64’). I have run np.isnan(mat.any()) #and gets False np.isfinite(mat.all()) #and gets True I tried using mat[np.isfinite(mat) == True] = … Read more
How do I save a trained Naive Bayes classifier to disk and use it to predict data? I have the following sample program from the scikit-learn website: from sklearn import datasets iris = datasets.load_iris() from sklearn.naive_bayes import GaussianNB gnb = GaussianNB() y_pred = gnb.fit(iris.data, iris.target).predict(iris.data) print “Number of mislabeled points : %d” % (iris.target != … Read more
