AI News, Bugra Akyildiz - A Thorough Machine Learning Pipeline via Scikit Learn

Bugra Akyildiz - A Thorough Machine Learning Pipeline via Scikit Learn

Specifically, I will try to go over the following steps in Scikit-Learn: - Introduce various feature extraction methods for image and text - Explain how one might use various feature selection algorithms to capture information rich features and ignoring the irrelevant or redundant ones - Show various approaches and methods to do parameter optimization within Scikit-Learn - Explain and compare different validation score and metrics to evaluate the model accuracy - Introduce how one could do model selection - Show how one could deploy the model into production Then I will introduce more advanced features and methods: - Introduce pipeline structures and parameter optimization within the grid search - Randomized Search to make the parameter search more intelligibly and efficiently - Feature Unions to make the feature more diverse and rich.

Meet Michelangelo: Uber’s Machine Learning Platform

Year in Review: 2017 Highlights from Uber Open Source

Year in Review: 2017 Highlights from the Uber Engineering Blog

Managing Machine Learning Workflows with Scikit-learn Pipelines Part 2: Integrating Grid Search

In our last post we looked at Scikit-learn pipelines as a method for simplifying machine learning workflows.

Designed as a manageable way to apply a series of data transformations followed by the application of an estimator, pipelines were noted as being a simple tool useful mostly for: Another simple yet powerful technique we can pair with pipelines to improve performance is grid search, which attempts to optimize model hyperparameter combinations.

Exhaustive grid search -- as opposed to alternate hyperparameter combination optimization schemes such as randomized optimization -- tests and compares all possible combinations of desired hyperparameter values, an exercise in exponential growth.

specifies that two grids should be explored: one with a linear kernel and C values in [1, 10, 100, 1000], and the second one with an RBF kernel, and the cross-product of C values ranging in [1, 10, 100, 1000] and gamma values in [0.001, 0.0001].

Since our model uses a decision tree estimator, we will use grid search to optimize the following hyperparameters: Here is the code to use exhaustive grid search in our adapted pipeline example.

In order to score our resulting models (there are a potential 2 * 5 * 5 * 5 * 2 = 500), we will direct our grid search to evaluate them by their accuracy on the test set.

The script reports back the highest attained accuracy (0.925), which is clearly better than the default 0.867, for not much additional computation, at least not in absolute terms, given our toy dataset.

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Machine learning for neuroimaging with scikit-learn

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Andreas C.

PyStruct - Learning Structured Prediction in Python

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Hannes Schulz, Andreas Müller, and Sven Behnke:

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Exploiting local structure in stacked Boltzmann machines

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