Why Machine Learning is more Practical than Econometrics in the Real World — R-bloggers

[This article was first published on R – Remix Institute, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t. Motivation I’ve read several studies and articles that claim Econometric…

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Using Gradient Boosted Machine to Predict MPG for 2019 Vehicles — R-bloggers

Continuing on the below post, I am going to use a gradient boosted machine model to predict combined miles per gallon for all 2019 motor vehicles. Part 1: Using Decision Trees and Random Forest to Predict MPG for 2019 Vehicles The raw data is located on the EPA government site The variables/features I am using for the models are: Engine displacement (size), number of cylinders, transmission type, number of gears, air inspired method, regenerative braking type, battery capacity Ah, drivetrain, fuel type, cylinder deactivate, and variable valve. There are 1253 vehicles in the dataset (does not include pure electric vehicles) summarized below. fuel_economy_combined eng_disp num_cyl transmission Min. :11.00 Min. :1.000 Min. : 3.000 A :301 1st Qu.:19.00 1st Qu.:2.000 1st Qu.: 4.000 AM : 46 Median :23.00 Median :3.000 Median : 6.000 AMS: 87 Mean :23.32 Mean :3.063 Mean : 5.533 CVT: 50 3rd Qu.:26.00 3rd Qu.:3.600 3rd Qu.: 6.000 M :148 Max. :58.00 Max. :8.000 Max. :16.000 SA :555 SCV: 66 num_gears air_aspired_method Min. : 1.000 Naturally Aspirated :523 1st Qu.: 6.000 Other : 5 Median : 7.000 Supercharged : 55 Mean : 7.111 Turbocharged :663 3rd Qu.: 8.000 Turbocharged+Supercharged: 7 Max. :10.000 regen_brake batt_capacity_ah No :1194 Min. : 0.0000 Electrical Regen Brake: 57 1st Qu.: 0.0000 Hydraulic Regen Brake : 2 Median : 0.0000 Mean : 0.3618 3rd Qu.: 0.0000 Max. :20.0000 drive cyl_deactivate 2-Wheel Drive, Front :345 Y: 172 2-Wheel Drive, Rear :345 N:1081 4-Wheel Drive :174 All Wheel Drive :349 Part-time 4-Wheel Drive: 40 fuel_type Diesel, ultra low sulfur (15 ppm, maximum): 28 Gasoline (Mid Grade Unleaded Recommended) : 16 Gasoline (Premium Unleaded Recommended) :298 Gasoline (Premium Unleaded Required) :320 Gasoline (Regular Unleaded Recommended) :591 variable_valve N: 38 Y:1215 Starting with an untuned base model: trees boosted_stats_untuned RMSE Rsquared MAE 2.4262643 0.8350367 1.7513331 The untuned GBM model performs better than the multiple linear regression model, but worse than the random forest. I am going to tune the GBM by running a grid search: #create hyperparameter gridhyper_grid

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Using SVM to Predict MPG for 2019 Vehicles — R-bloggers

[This article was first published on Data Science, Data Mining and Predictive Analytics, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don’t. Continuing on the below post, I am…

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Three Strategies for Working with Big Data in R — R-bloggers

(This article was first published on R Views, and kindly contributed to R-bloggers) For many R users, it’s obvious why you’d want to use R with big data, but not so obvious how. In fact, many people (wrongly) believe that R just doesn’t work very well for big data. In this article, I’ll share three…

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Pronóstico de Series Temporales con Redes Neuronales en Python — Aprende Machine Learning

En el artículo de hoy veremos qué son las series temporales y cómo predecir su comportamiento utilizando redes neuronales con Keras y Tensorflow. Repasaremos el código completo en Python y la descarga del archivo csv del ejercicio propuesto con los datos de entrada. ¿Qué es una serie temporal y qué tiene de especial? Una serie…

via Pronóstico de Series Temporales con Redes Neuronales en Python — Aprende Machine Learning

Machine Learning Basics – Gradient Boosting & XGBoost — R-bloggers

(This article was first published on Shirin’s playgRound, and kindly contributed to R-bloggers) In a recent video, I covered Random Forests and Neural Nets as part of the codecentric.ai Bootcamp. In the most recent video, I covered Gradient Boosting and XGBoost. You can find the video on YouTube and the slides on slides.com. Both are…

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¿Cómo funcionan las Convolutional Neural Networks? Visión por Ordenador — Aprende Machine Learning

En este artículo intentaré explicar la teoría relativa a las Redes Neuronales Convolucionales (en inglés CNN) que son el algoritmo utilizado en Aprendizaje Automático para dar la capacidad de “ver” al ordenador. Gracias a esto, desde apenas 1998, podemos clasificar imágenes, detectar diversos tipos de tumores automáticamente, enseñar a conducir a los coches autónomos y…

via ¿Cómo funcionan las Convolutional Neural Networks? Visión por Ordenador — Aprende Machine Learning