classification

adding words to stop_words list in TfidfVectorizer in sklearn

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This is how you can do it: from sklearn.feature_extraction import text from sklearn.feature_extraction.text import TfidfVectorizer my_stop_words = text.ENGLISH_STOP_WORDS.union([“book”]) vectorizer = TfidfVectorizer(ngram_range=(1,1), stop_words=my_stop_words) X = vectorizer.fit_transform([“this is an apple.”,”this is a book.”]) idf_values = dict(zip(vectorizer.get_feature_names(), vectorizer.idf_)) # printing the tfidf vectors print(X) # printing the vocabulary print(vectorizer.vocabulary_) In this example, I created the tfidf vectors for … Read more

Predicting how long an scikit-learn classification will take to run

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There are very specific classes of classifier or regressors that directly report remaining time or progress of your algorithm (number of iterations etc.). Most of this can be turned on by passing verbose=2 (any high number > 1) option to the constructor of individual models. Note: this behavior is according to sklearn-0.14. Earlier versions have … Read more

General approach to developing an image classification algorithm for Dilbert cartoons

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So i think you are on the right track w/r/t your step 1 (apply some algorithm to the image, which converts it into a set of features). This project is more challenging that most ML problems because here you will actually have to create your training data set from the raw data (the individual frames … Read more

Precision/recall for multiclass-multilabel classification

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For multi-label classification you have two ways to go First consider the following. is the number of examples. is the ground truth label assignment of the example.. is the example. is the predicted labels for the example. Example based The metrics are computed in a per datapoint manner. For each predicted label its only its … Read more

Compute class weight function issue in ‘sklearn’ library when used in ‘Keras’ classification (Python 3.8, only in VS code)

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After spending a lot of time, this is how I fixed it. I still don’t know why but when the code is modified as follows, it works fine. I got the idea after seeing this solution for a similar but slightly different issue. class_weights = compute_class_weight( class_weight = “balanced”, classes = np.unique(train_classes), y = train_classes … Read more

sklearn LogisticRegression and changing the default threshold for classification

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I would like to give a practical answer from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score, confusion_matrix, recall_score, roc_auc_score, precision_score import numpy as np X, y = make_classification( n_classes=2, class_sep=1.5, weights=[0.9, 0.1], n_features=20, n_samples=1000, random_state=10 ) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42) clf = … Read more

What are the 15 classifications of types in C++?

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I spoke with Walter directly, and it was simply a miscount. “Alas, I realized shortly thereafter that I’d miscounted and hence committed an off-by-one error during the talk: there are 14 (not 15) type classifications. See the list of primary type category predicates in clause [meta.unary.cat] in the C++ standard; these correspond to the classifications … Read more