AUC-ROC Curve – The Star Performer! You’ve built your machine learning model – so what’s next? You need to evaluate…

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## Everything you Should Know about Confusion Matrix for Machine Learning

Confusion Matrix – Not So Confusing! Have you been in a situation where you expected your machine learning model to…

Continue Reading## How to Configure XGBoost for Imbalanced Classification

The XGBoost algorithm is effective for a wide range of regression and classification predictive modeling problems. It is an efficient…

Continue Reading## ROC Curves and Precision-Recall Curves for Imbalanced Classification

Most imbalanced classification problems involve two classes: a negative case with the majority of examples and a positive case with…

Continue Reading## Is Random Forest better than Logistic Regression? (a comparison)

(a comparison)Delving into the nature of random forest, walking through an example, and comparing it to logistic regression. Andrew HershyBlockedUnblockFollowFollowingJul…

Continue Reading## Predicting Cancer with Logistic Regression in Python

Predicting Cancer with Logistic Regression in PythonUnderstanding the data, logistic regression, testing data, confusion matrices, ROC curveAndrew HershyBlockedUnblockFollowFollowingJul 1SourceIntroduction:In my first logistic…

Continue Reading## Illustrating Predictive Models with the ROC Curve

For example, we would all be furious if our email program’s spam classifier was only able to detect 50% of…

Continue Reading## Positive or Negative? Spam or Not-spam? A simple Text classification problem using Python

First, we’ll learn what text classification really means. What is text classification?Text Classification(TC) is the process of assigning tags or…

Continue Reading## Determining the Happiest Cities using Twitter Sentiment Analysis with BERT

In this article, we examine the state-of-the-art technology in Deep Learning today to determine the positivity of users tweets in…

Continue Reading## A new Tool to your Toolkit, KL Divergence at Work

A new Tool to your Toolkit, KL Divergence at WorkThe finale, applying KL Divergence to real DatasetAbhishek MungoliBlockedUnblockFollowFollowingJun 15In my previous post,…

Continue Reading## Preventing Discriminatory Outcomes in Credit Models

Preventing Discriminatory Outcomes in Credit ModelsValeria CortezBlockedUnblockFollowFollowingJun 5Machine learning is being deployed to do large-scale decision making, which can strongly impact…

Continue Reading## Measuring Performance: AUC (AUROC)

Measuring Performance: AUC (AUROC)Rachel Ballantyne DraelosBlockedUnblockFollowFollowingFeb 23The area under the receiver operating characteristic (AUROC) is a performance metric that you can…

Continue Reading## Recall, Precision, F1, ROC, AUC, and everything

Recall, Precision, F1, ROC, AUC, and everythingOfir Shalev (@ofirdi)BlockedUnblockFollowFollowingMay 27Your boss asked you to build a fraud detection classifier, so…

Continue Reading## An Introduction to Evaluating Classification Models

Let’s look at the actual distribution of outcome. Target Class Distribution of Full Datasetsns. countplot(x=fraud['Class'])Here, the class imbalance, or the skewed…

Continue Reading## Using Natural Language Processing To Rate The Sentiment of the Game of Thrones Finale

Using Natural Language Processing To Rate The Sentiment of the Game of Thrones FinaleUsing Vader and TextBlob to analyze the sentiment…

Continue Reading## ROC Curves and the Efficient Frontier

In that case you catch every good investment but incur a significant cost. Also note that in the example ROC…

Continue Reading## Challenges in sentiment analysis: a case for word clouds (for now)

For example, if I only take the list of positive tweets and I get an aggregate sentiment score for the…

Continue Reading## On Invariants.

On Invariants. What is an invariant?Andrew PritchardBlockedUnblockFollowFollowingMay 10The specification of a program should be its class invariantsAim to write programs…

Continue Reading## Validating Type I and II Errors in A/B Tests in R

Validating Type I and II Errors in A/B Tests in R#ODSC – Open Data ScienceBlockedUnblockFollowFollowingMay 10In the below work, we will…

Continue Reading## Text Classification Every Engineer should be able to Build from Scratch — Naive Bayes

Or what is the relationship between each word and the review sentimental?NB is simply a systematic way of making predictions…

Continue Reading## How to assess a binary Logistic Regressor with scikit-learn

How to assess a binary Logistic Regressor with scikit-learnBookmark this python function that makes assessing your binary classifier easy. Greg…

Continue Reading## How to interpret a binary Logistic Regressor with scikit-learn

How to interpret a binary Logistic Regressor with scikit-learnGreg ConditBlockedUnblockFollowFollowingApr 17Functionality OverviewLogistic Regression is a valuable classifier for its interpretability.…

Continue Reading## Sentiment Analysis: Beyond Words

First, I split each review into sentences, and used spaCy and gensim to get distinct topics that reviewers mentioned in…

Continue Reading## Binary Classifier Evaluation made easy with HandySpark

Binary Classifier Evaluation made easy with HandySparkDaniel GodoyBlockedUnblockFollowFollowingMar 11Photo by SJ Baren on UnsplashTLDR;HandySpark is a Python package designed to improve…

Continue Reading## Demystifying Support Vector Machines

+ is 1. 5 and that is exactly the value of ζ in this case. Understanding CAs I mentioned above…

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