scikit-learn - w3toppers.com

How to get most informative features for scikit-learn classifier for different class?

In the case of binary classification, it seems like the coefficient array has been flatten. Let’s try to relabel our data with only two labels: import codecs, re, time from itertools import chain import numpy as np from sklearn.feature_extraction.text import CountVectorizer from sklearn.naive_bayes import MultinomialNB trainfile=”train.txt” # Vectorizing data. train = [] word_vectorizer = CountVectorizer(analyzer=”word”) … Read more

Pyinstaller ; ModuleNotFoundError: No module named ‘sklearn.utils._cython_blas’

PyInstaller uses a hook mechanism for each Python module, but sometimes it misses some internal packages so you need to provide them manually. You can use –hidden-import to add sklearn‘s missing modules. pyinstaller -F –hidden-import=”sklearn.utils._cython_blas” –hidden-import=”sklearn.neighbors.typedefs” –hidden-import=”sklearn.neighbors.quad_tree” –hidden-import=”sklearn.tree._utils” Datamanager.py

How to get Predictions with XGBoost and XGBoost using Scikit-Learn Wrapper to match?

Please look at this answer here xgboost.train will ignore parameter n_estimators, while xgboost.XGBRegressor accepts. In xgboost.train, boosting iterations (i.e. n_estimators) is controlled by num_boost_round(default: 10) It suggests to remove n_estimators from params supplied to xgb.train and replace it with num_boost_round. So change your params like this: params = {‘objective’: ‘reg:linear’, ‘max_depth’: 2, ‘learning_rate’: .1, ‘min_child_weight’: … Read more

Scikit learn – fit_transform on the test set

You are not supposed to do fit_transform on your test data, but only transform. Otherwise, you will get different vectorization than the one used during training. For the memory issue, I recommend TfIdfVectorizer, which has numerous options of reducing the dimensionality (by removing rare unigrams etc.). UPDATE If the only problem is fitting test data, … Read more

use Featureunion in scikit-learn to combine two pandas columns for tfidf

FeatureUnion was not meant to be used that way. It instead takes two feature extractors / vectorizers and applies them to the input. It does not take data in the constructor the way it is shown. CountVectorizer is expecting a sequence of strings. The easiest way to provide it with that is to concatenate the … Read more

Can I send callbacks to a KerasClassifier?

Reading from here, which is the source code of KerasClassifier, you can pass it the arguments of fit and they should be used. I don’t have your dataset so I cannot test it, but you can tell me if this works and if not I will try and adapt the solution. Change this line : … Read more

Scikits-Learn RandomForrest trained on 64bit python wont open on 32bit python

This occurs because the random forest code uses different types for indices on 32-bit and 64-bit machines. This can, unfortunately, only be fixed by overhauling the random forests code. Since several scikit-learn devs are working on that anyway, I put it on the todo list. For now, the training and testing machines need to have … Read more

In sklearn.decomposition.PCA, why are components_ negative?

As you figured out in your answer, the results of a singular value decomposition (SVD) are not unique in terms of singular vectors. Indeed, if the SVD of X is \sum_1^r \s_i u_i v_i^\top : with the s_i ordered in decreasing fashion, then you can see that you can change the sign (i.e., “flip”) of … Read more

Python import error: cannot import name ‘six’ from ‘sklearn.externals’

Solution: The real answer is that the dependency needs to be changed by the mlrose maintainers. A workaround is: import six import sys sys.modules[‘sklearn.externals.six’] = six import mlrose

Using scikit to determine contributions of each feature to a specific class prediction

Update Being more knowledgable about ML today than I was 2.5 years ago, I will now say this approach only works for highly linear decision problems. If you carelessly apply it to a non-linear problem you will have trouble. Example: Imagine a feature for which neither very large nor very small values predict a class, … Read more