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Rohan Kotwani's Blog (3)

High Density Region Estimation with KernelML

Data scientists and predictive modelers often use 1-D and 2-D aggregate statistics for exploratory analysis, data cleaning, and feature creation. Higher dimensional aggregations, i.e., 3 dimensional and above, are more difficult to visualize and understand. High density regions are one example of these N-dimensional statistics. High density regions can be useful for summarizing common characteristics across multiple variables. Another use case is to validate a forecast prediction’s…

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Added by Rohan Kotwani on January 3, 2019 at 4:00pm — No Comments

Generalized Machine Learning - Kerneml - Simple ML to train Complex ML

I recently created a ‘particle optimizer’ and published a pip python package called kernelml. The motivation for making this algorithm was to give analysts and data scientists a generalized machine learning algorithm for complex loss functions and non-linear coefficients. The optimizer uses a combination of simple machine learning and probabilistic simulations to search for optimal parameters using a loss function, input and output matrices, and (optionally) a random…

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Added by Rohan Kotwani on May 4, 2018 at 10:00am — 2 Comments

How signal processing can be used to identify patterns in complex time series

The trend and seasonality can be accounted for in a linear model by including sinusoidal components with a given frequency. However, finding the appropriate frequency…

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Added by Rohan Kotwani on January 18, 2017 at 6:30pm — 3 Comments

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