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Machine Learning Paradigms with Example

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Machine Learning is the method of teaching computer programs to do a specific task accurately (essentially a prediction) by training a predictive model using various statistical algorithms leveraging data. Source: [link] For […].

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Bivariate Feature Analysis in Python

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Feature analysis is an important step in building any predictive model. It helps us in understanding the relationship between dependent and independent variables.

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Quick Guide to Evaluation Metrics for Supervised and Unsupervised Machine Learning

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Introduction Machine learning is about building a predictive model using historical data. The post Quick Guide to Evaluation Metrics for Supervised and Unsupervised Machine Learning appeared first on Analytics Vidhya.

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STANDARDIZED VS UNSTANDARDIZED REGRESSION COEFFICIENT

Analytics Vidhya

ArticleVideo Book This article was published as a part of the Data Science Blogathon. Introduction Some time back, I was making the predictive model. The post STANDARDIZED VS UNSTANDARDIZED REGRESSION COEFFICIENT appeared first on Analytics Vidhya.

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The quest for high-quality data

O'Reilly on Data

The good news is that researchers from academia recently managed to leverage that large body of work and combine it with the power of scalable statistical inference for data cleaning. HoloClean adopts the well-known “noisy channel” model to explain how data was generated and how it was “polluted.”

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What to Do When AI Fails

O'Reilly on Data

And last is the probabilistic nature of statistics and machine learning (ML). Most AI models decay overtime: This phenomenon, known more widely as model decay , refers to the declining quality of AI system results over time, as patterns in new data drift away from patterns learned in training data.

Risk 364
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Proposals for model vulnerability and security

O'Reilly on Data

The objective here is to brainstorm on potential security vulnerabilities and defenses in the context of popular, traditional predictive modeling systems, such as linear and tree-based models trained on static data sets. If an attacker can receive many predictions from your model API or other endpoint (website, app, etc.),

Modeling 272