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Ramblings of a rogue Mathematician

Logistic Regression: Classification of Wine Quality

In the previous post, we trained DynaML’s feed forward neural networks on the wine quality data set. Lets compare how single layer feed forward neural networks compare to a simple logistic regression trained using Gradient Descent. The TestLogisticWineQuality program in the examples package does precisely that (check out the source code below). Red Wine TestLogisticWineQuality(stepSize = 0.2, maxIt = 120, mini = 1.0, training = 800, test = 800, regularization = 0.2, wineType = "red")...

Neural Networks: Classification of Wine Quality

The wine quality data set is a common example used to benchmark classification models. Here we use the DynaML scala machine learning environment to train classifiers to detect ‘good’ wine from ‘bad’ wine. A short listing of the data attributes/columns is given below. The UCI archive has two files in the wine quality data set namely winequality-red.csv and winequality-white.csv. We train two separate classification models, one for red wine and one for white. Data Set...

System Identification using Gaussian Processes: Abott Power Plant, Champaign, Illinois

In this post, we use the DynaML Scala machine learning environment to train Gaussian Process models to analyse time series data taken from a coal power plant. The Data Set From the Daisy system identification database, we download the abott power plant data. The data characteristics are summarized below. Description: The data comes from a model of a Steam Generator at Abbott Power Plant in Champaign IL. Sampling Frequency: 3 sec Number: 9600 Inputs: 1....