Python WebApp: Learn how to serve a Machine Learning Model predicting car prices (Full stack Book 1) by Maignan Nicolas

Author:Maignan, Nicolas [Maignan, Nicolas]
Language: eng
Format: epub, azw3, pdf
Published: 2020-05-29T16:00:00+00:00

from sklearn.model_selection import GridSearchCV

params = {

"boosting" : "gbdt" , # gdbt, rf, goss or dart

"objective" : "regression" ,

"metrics" : ["l2_root" , "l1" ], # metric(s) to be evaluated on the evaluation set(s)

"num_threads" : 2 , # number of threads for LightGBM (set to available real CPU cores)

"num_leaves" : 128 , # max number of leaves in one tree

"max_depth" : -1 , # limit the max depth for tree model

"learning_rate" : 0.1 , # shrinkage rate

"feature_fraction" : 1 , # if you set it to 0.8, LightGBM will select 80% of features before training each tree

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