As already discussed, tensorflow doesn’t provide its own way to cross-validate the model. The recommended way is to use KFold. It’s a bit tedious, but doable. Here’s a complete example of cross-validating MNIST model with tensorflow and KFold: from sklearn.model_selection import KFold import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data # Parameters learning_rate = 0.01 … Read more
Am I right to say that also Standardization gets affected negatively by the extreme values as well? Indeed you are; the scikit-learn docs themselves clearly warn for such a case: However, when data contains outliers, StandardScaler can often be mislead. In such cases, it is better to use a scaler that is robust against outliers. … Read more
You can use scikit-learn’s mutual_info_classif here is an example from sklearn.datasets import fetch_20newsgroups from sklearn.feature_selection import mutual_info_classif from sklearn.feature_extraction.text import CountVectorizer categories = [‘talk.religion.misc’, ‘comp.graphics’, ‘sci.space’] newsgroups_train = fetch_20newsgroups(subset=”train”, categories=categories) X, Y = newsgroups_train.data, newsgroups_train.target cv = CountVectorizer(max_df=0.95, min_df=2, max_features=10000, stop_words=”english”) X_vec = cv.fit_transform(X) res = dict(zip(cv.get_feature_names(), mutual_info_classif(X_vec, Y, discrete_features=True) )) print(res) this will output … Read more
The number of points depend on the number of unique values in the input. Since the input vector has only 2 unique values, the function gives correct output.
The Imputer fills missing values with some statistics (e.g. mean, median, …) of the data. To avoid data leakage during cross-validation, it computes the statistic on the train data during the fit, stores it and uses it on the test data, during the transform. from sklearn.preprocessing import Imputer obj = Imputer(strategy=’mean’) obj.fit([[1, 2, 3], [2, … Read more
I finally got to solve the problem. Two things had to be done: train_argcands_target is a list and it has to be a numpy array. I’m surprised it worked well before when I just used the estimator directly. For some reason (I don’t know why, yet), it doesn’t work either if I use the sparse … Read more
MLPClassifier is not yet available in scikit-learn v0.17 (as of 1 Dec 2015). If you really want to use it you could clone 0.18dev (however, I don’t know how stable this branch currently is).
There are several things wrong in the code you posted: The keys of the param_grid dictionary need to be strings. You should be getting a NameError. The key “abc__n_estimators” should just be “n_estimators”: you are probably mixing this with the pipeline syntax. Here nothing tells Python that the string “abc” represents your AdaBoostClassifier. None (and … Read more
Here are a couple of approaches: Find the ratio of number of unique values to the total number of unique values. Something like the following likely_cat = {} for var in df.columns: likely_cat[var] = 1.*df[var].nunique()/df[var].count() < 0.05 #or some other threshold Check if the top n unique values account for more than a certain proportion … Read more