The performance of a machine learning model can be characterized in terms of the bias and the variance of the…
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Bias and Variance in Machine Learning – A Fantastic Guide for Beginners!
OverviewLearn to interpret Bias and Variance in a given model. What is the difference between Bias and Variance?How to achieve…
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Remember that component 1 is the principal component with the highest variance (since highest variance equates to highest potential signal).…
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Bias and variance in linear modelsA look at the bias and variance tradeoff for linear modelsNischal MBlockedUnblockFollowFollowingJun 24I am sure everyone has…
Continue ReadingThe Bias-Variance trade-off : Explanation and Demo
The Bias-Variance trade-off : Explanation and DemoAditya PrasadBlockedUnblockFollowFollowingApr 5The Bias-Variance trade-off is a basic yet important concept in the field of data…
Continue ReadingBias-Variance Tradeoff to Avoid under/over-fitting
Bias-Variance Tradeoff to Avoid under/over-fittingA fundamental concept to understand your model’s performance. MaherBlockedUnblockFollowFollowingApr 3The irreducible errorI think most of us…
Continue ReadingUnderstanding Variance and Covariance
Consider I have three weights that are exactly the same and I want to balance them. for that, I need…
Continue ReadingWhy Sample Variance is Divided by n-1
Why Sample Variance is Divided by n-1Explaining high school statistics that your teachers didn’t teachEden AuBlockedUnblockFollowFollowingFeb 20Photo by Tim Bennett on UnsplashIf you…
Continue ReadingPrincipal Component Analysis (PCA) 101, using R
PCA can reduce dimensionality but it wont reduce the number of features / variables in your data. What this means…
Continue ReadingTrade-offs: How to aim for the sweet spot.
Low Bias and high variance overfits data as the model pays way too much attention towards training data & doesn't…
Continue ReadingUsing Analysis of Variance with Digital Experimentation Data
Given that Optimizely’s stats engine uses a sequential approach to AB testing to compare the means of two or more…
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