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As seen in: Towards Data Science
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Articles by KNeighbors Classifier

Naive Bayes Classification

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By Roi Yehoshua, AdaBoost Classifier, KNeighbors Classifier, Random ForestClassifier
| Towards Data Science
In-depth explanation of the Naive Bayes family of classifiers, including a text classification example in Python Naive Bayes classifiers is a family of probabilistic classifiers that are based on applying Bayes’ theorem with naive assumption on independence between the features. These classifiers are extremely fast both in training and prediction, and they are also highly scalable and interpretable.
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Naive Bayes Classification

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By Roi Yehoshua, AdaBoost Classifier, KNeighbors Classifier, Random ForestClassifier
| Towards Data Science
The event models described above can also be combined in case we have a heterogenous data set, i.e., a data set that contains different types of features (for example, both categorical and continuous features). The module sklearn.naive_bayes provides implementations for all the four Naive Bayes classifiers mentioned above: implements the Bernoulli Naive Bayes model. implements the categorical Naive Bayes model. implements the multinomial Naive Bayes model. implements the Gaussian Naive Bayes model.
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31 Uniques Python Packages To Improve Your Data Workflow

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By Cornellius Yudha Wijaya, KNeighbors Classifier
| Towards Data Science
Various Python packages for data people Data is a vast field with big community support the technology development. Furthermore, Python has avid supporter that helps the data world become more accessible and brings value to the data workflow. Various Python packages have been developed to help data people in their works. In my experience, many useful data Python packages lack recognition or still growing in popularity.
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Ensemble Learning in sklearn

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By Wei-Meng Lee, Transformer Mixin, Use GridSearchCV, KNeighbors Classifier
| Towards Data Science
Photo by Nick Fewings on Unsplash In my previous article, I talked about using pipelines in sklearn: In that article, you learned how to use pipelines in sklearn to streamline your machine learning workflow. You also learned how to use GridSearchCV() together with pipelines to find the best estimator for your dataset.
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