{"metadata":{"kernelspec":{"name":"ir","display_name":"R","language":"R"},"language_info":{"name":"R","codemirror_mode":"r","pygments_lexer":"r","mimetype":"text/x-r-source","file_extension":".r","version":"3.6.3"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":21651,"databundleVersionId":1595136,"sourceType":"competition"}],"dockerImageVersionId":30040,"isInternetEnabled":true,"language":"r","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"**I found a python script and translated to R**","metadata":{}},{"cell_type":"code","source":"# install.packages(\"ISLR2\")\nlibrary(ISLR2)\nGitters <- na.omit(Hitters)\nn <- nrow(Gitters)\nset.seed(13)\nntest <- trunc(n / 3)\ntestid <- sample(1:n, ntest)\n###\nlfit <- lm(Salary ~ ., data = Gitters[-testid, ])\nlpred <- predict(lfit, Gitters[testid, ])\nwith(Gitters[testid, ], mean(abs(lpred - Salary)))\n###\nx <- scale(model.matrix(Salary ~ . - 1, data = Gitters))\ny <- Gitters$Salary\n###\nlibrary(glmnet)\ncvfit <- cv.glmnet(x[-testid, ], y[-testid],\n                   type.measure = \"mae\")\ncpred <- predict(cvfit, x[testid, ], s = \"lambda.min\")\nmean(abs(y[testid] - cpred))\n###\nlibrary(keras)\n# reticulate::use_condaenv(condaenv = \"r-tensorflow\")\nmodnn <- keras_model_sequential() %>%\n  layer_dense(units = 50, activation = \"relu\",\n              input_shape = ncol(x)) %>%\n  layer_dropout(rate = 0.4) %>%\n  layer_dense(units = 1)\n\nprint(modnn)","metadata":{"execution":{"iopub.status.busy":"2024-04-26T14:25:30.281138Z","iopub.execute_input":"2024-04-26T14:25:30.28337Z","iopub.status.idle":"2024-04-26T14:25:44.826624Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stderr","text":"Installing package into ‘/usr/local/lib/R/site-library’\n(as ‘lib’ is unspecified)\n\n","output_type":"stream"},{"output_type":"display_data","data":{"text/html":"254.668652883752","text/markdown":"254.668652883752","text/latex":"254.668652883752","text/plain":"[1] 254.6687"},"metadata":{}},{"output_type":"display_data","data":{"text/html":"252.299368270238","text/markdown":"252.299368270238","text/latex":"252.299368270238","text/plain":"[1] 252.2994"},"metadata":{}},{"name":"stdout","text":"Model\nModel: \"sequential\"\n________________________________________________________________________________\nLayer (type)                        Output Shape                    Param #     \n================================================================================\ndense_1 (Dense)                     (None, 50)                      1050        \n________________________________________________________________________________\ndropout (Dropout)                   (None, 50)                      0           \n________________________________________________________________________________\ndense (Dense)                       (None, 1)                       51          \n================================================================================\nTotal params: 1,101\nTrainable params: 1,101\nNon-trainable params: 0\n________________________________________________________________________________\n\n\n","output_type":"stream"}]}]}