The gradient boosting yields a better recall score but performs poorer than the logistic regression in terms of accuracy and precision.

Here we compare the performance of gradient boosting classifier with logistic regression. We use the same data set as this post

In this post, we described logistic regression. This post discusses logistic regression with regularization.

Using the same dataset,

In the following post

We discussed the IEBAE framework using a simple regression example. However, we did not tune the hyperparameters in the gradient boosting approach. In this post, we expand the discussion on the gradient boosting example using the standard KFold cross-validation approach.

We import all necessary packages (mltools

This article is adapted from the physics-based machine learning library ADCME documentation

What is Inverse Modeling?

The inverse modeling problem can be mathematically formulated as finding an unknown parameter X given input X and output u of a forward model

u=F(θ, X)

Here X and u can be a sample from a stochastic process…

Inverse modeling is everywhere in scientific computing. TensorFlow and PyTorch are everywhere in deep learning. ADCME.jl is the dealer that brings the power of techniques from machine learning to inverse modeling in scientific computing. https://github.com/kailaix/ADCME.jl

Inverse Modeling in Scientific Computing

Many scientific computing problems in physics, chemistry, biology and so on can be formulated as…

How can neural networks stack up against parametrization methods used in engineering developed over last half century? A tentative answer is: Universal approximation, implicit regularization, alleviating the curse of dimensionality and efficient computing frameworks!

Physics and Partial Differential Equations

In order to carry out quantitative analysis, researchers usually apply different mathematical models for their problems…

KLDIV

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