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All Blog Posts Tagged 'Models' (9)

A short introduction to Log Models

Why do we take logs of variable in Regression analysis?

We should remember that a regression equation has two parts

i) The Dependent variable (Predictand)

ii) The Independent variables (Predictors) ; which can be one or more and can be of different types (Categorical or Continuous).

The nature of the regression that we should run depends on the type of Dependent variable that we are dealing with in our model. For example, if the dependent variable is Continuous…

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Added by Sibashis Chakraborty on October 20, 2019 at 8:57am — No Comments

Create Transformed, N-Dimensional Polygons with Covariance Matrix

The covariance matrix has many interesting properties, and it can be found in mixture models, component analysis, Kalman filters, and more. Developing an intuition for how the covariance matrix operates is useful in understanding its practical implications. This article will focus on a few important properties, associated proofs, and then some interesting practical applications, i.e., extracting transformed polygons from a Gaussian mixture's covariance matrix.

I have often found that…

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Added by Rohan Kotwani on May 26, 2019 at 7:30am — No Comments

So, How Many ML Models You Have NOT Built?

What a weird question. That’s what you would have thought after reading the headline. Perhaps you thought the word “NOT” was accidental.

Hmm, for past few years many of us have come across articles like

  • “Top 10 Machine Learning Algorithms every Data Scientist …
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Added by Venkat Raman on February 6, 2018 at 12:30am — 3 Comments

Untestable & Unreasonable Assumptions in Models is Data Science Malpractice

We all know models are always an illusion of reality - yet may or may not be useful. Data scientists should build both simple and complex models with reasonable and testable assumptions that capture what is…

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Added by Michael Walker on December 5, 2014 at 6:31pm — No Comments

Forecasting with the Baum-Welch Algorithm and Hidden Markov Models

Leonard Baum and Lloyd Welch designed a probabilistic modelling algorithm to detect patterns in Hidden Markov Processes. They built upon the theory of probabilistic functions of a …

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Added by Michael Walker on February 24, 2014 at 10:02pm — 1 Comment

Predicting the Super Bowl

NFL 2013 Team Expected Points Added (EPA) per game - Defense by Offense

The atavistic love of sport, strategy,…

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Added by Michael Walker on January 27, 2014 at 7:00pm — No Comments

Models vs. Experiments

At the Rose Professional Data Science Practice, we see many organizations spending a majority of…
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Added by Michael Walker on November 17, 2013 at 8:30am — No Comments

Tool for Computing Continuous Distributed Representations of Words

Natural language processing (NLP) involves machine learning, artificial intelligence, algorithms and linguistics related to interactions between computers and human languages. One important goal…

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Added by Michael Walker on August 20, 2013 at 7:27pm — No Comments

Data Science Ethics & Big Data Business Models

Ray Wang's HBR piece "What a Big-Data Business Model Looks Like" asserts three basic information…

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Added by Michael Walker on January 10, 2013 at 9:30am — No Comments

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