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Prediction Intervals for Machine Learning

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A prediction interval is calculated as some combination of the estimated variance of the model and the variance of the outcome variable. Prediction intervals are easy to describe, but difficult to calculate in practice. In simple cases like linear regression, we can estimate the confidence interval directly. In the cases of nonlinear regression algorithms, such as artificial neural networks, it is a lot more challenging and requires the choice and implementation of specialized techniques. General techniques such as the bootstrap resampling method can be used, but are computationally expensive to calculate. The paper "A Comprehensive Review of Neural Network-based Prediction Intervals and New Advances" provides a reasonably recent study of prediction intervals for nonlinear models in the context of neural networks.


How to Manually Optimize Neural Network Models

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Deep learning neural network models are fit on training data using the stochastic gradient descent optimization algorithm. Updates to the weights of the model are made, using the backpropagation of error algorithm. The combination of the optimization and weight update algorithm was carefully chosen and is the most efficient approach known to fit neural networks. Nevertheless, it is possible to use alternate optimization algorithms to fit a neural network model to a training dataset. This can be a useful exercise to learn more about how neural networks function and the central nature of optimization in applied machine learning. It may also be required for neural networks with unconventional model architectures and non-differentiable transfer functions.


Blending Ensemble Machine Learning With Python

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Blending is an ensemble machine learning algorithm. It is a colloquial name for stacked generalization or stacking ensemble where instead of fitting the meta-model on out-of-fold predictions made by the base model, it is fit on predictions made on a holdout dataset. Blending was used to describe stacking models that combined many hundreds of predictive models by competitors in the $1M Netflix machine learning competition, and as such, remains a popular technique and name for stacking in competitive machine learning circles, such as the Kaggle community. In this tutorial, you will discover how to develop and evaluate a blending ensemble in python. Blending Ensemble Machine Learning With Python Photo by Nathalie, some rights reserved.


Confidence Intervals for Machine Learning

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The value of a confidence interval is its ability to quantify the uncertainty of the estimate. It provides both a lower and upper bound and a likelihood. Taken as a radius measure alone, the confidence interval is often referred to as the margin of error and may be used to graphically depict the uncertainty of an estimate on graphs through the use of error bars. Often, the larger the sample from which the estimate was drawn, the more precise the estimate and the smaller (better) the confidence interval. We can also say that the CI tells us how precise our estimate is likely to be, and the margin of error is our measure of precision.


TensorFlow 2 Tutorial: Get Started in Deep Learning With tf.keras

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You can easily create learning curves for your deep learning models. First, you must update your call to the fit function to include reference to a validation dataset. This is a portion of the training set not used to fit the model, and is instead used to evaluate the performance of the model during training.