Created
August 28, 2019 04:27
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R TensorFlow Multiple Linear Regression
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| library(tfestimators) | |
| library(caret) | |
| #input_fn for a given subset of data | |
| cars_19_input_fn <- function(data, num_epochs = 1) { | |
| input_fn( | |
| data, | |
| features = colnames(cars_19[c(2:12)]), | |
| response = "fuel_economy_combined", | |
| batch_size = 64, | |
| num_epochs = num_epochs | |
| ) | |
| } | |
| cols <- feature_columns( | |
| column_numeric(colnames(cars_19[c(2, 3, 5, 8)])), | |
| column_categorical_with_identity("transmission", num_buckets = 7), | |
| column_categorical_with_identity("air_aspired_method", num_buckets = 5), | |
| column_categorical_with_identity("regen_brake", num_buckets = 3), | |
| column_categorical_with_identity("drive", num_buckets = 5), | |
| column_categorical_with_identity("fuel_type", num_buckets = 5), | |
| column_categorical_with_identity("cyl_deactivate", num_buckets = 2), | |
| column_categorical_with_identity("variable_valve", num_buckets = 2) | |
| ) | |
| model <- linear_regressor(feature_columns = cols) | |
| set.seed(123) | |
| indices <- sample(1:nrow(cars_19), size = 0.75 * nrow(cars_19)) | |
| train <- cars_19[indices, ] | |
| test <- cars_19[-indices, ] | |
| #train model | |
| model %>% train(cars_19_input_fn(train, num_epochs = 1000)) | |
| #evaluate model | |
| model %>% evaluate(cars_19_input_fn(test)) | |
| #predict | |
| yhat <- model %>% predict(cars_19_input_fn(test)) | |
| yhat <- unlist(yhat) | |
| y <- test$fuel_economy_combined | |
| postResample(yhat, y) | |
| r <- y - yhat | |
| plot(y, yhat, xlab = "actual", ylab = "predicted", main = "Multiple Linear Regression") | |
| abline(lm(yhat ~ y)) | |
| plot(r) | |
| #RMSE Rsquared MAE | |
| #2.5583185 0.7891934 1.9381757 |
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