There are several things wrong in the code you posted: The keys of the param_grid dictionary need to be strings. You should be getting a NameError. The key “abc__n_estimators” should just be “n_estimators”: you are probably mixing this with the pipeline syntax. Here nothing tells Python that the string “abc” represents your AdaBoostClassifier. None (and … Read more
[Answer after the question was edited & clarified:] Before rushing into implementation issues, it is always a good practice to take some time to think about the methodology and the task itself; arguably, intermingling early stopping with the cross validation procedure is not a good idea. Let’s make up an example to highlight the argument. … Read more
I would really advise against using OOB to evaluate a model, but it is useful to know how to run a grid search outside of GridSearchCV() (I frequently do this so I can save the CV predictions from the best grid for easy model stacking). I think the easiest way is to create your grid … Read more
Yes, it can be done, but with imblearn Pipeline. You see, imblearn has its own Pipeline to handle the samplers correctly. I described this in a similar question here. When called predict() on a imblearn.Pipeline object, it will skip the sampling method and leave the data as it is to be passed to next transformer. … Read more
Update: Ideally, the answer below should not be used as it leads to data leakage as discussed in comments. In this answer, GridSearchCV will tune the hyperparameters on the data already preprocessed by StandardScaler, which is not correct. In most conditions that should not matter much, but algorithms which are too sensitive to scaling will … Read more
The code shown by @sascha is correct. However, the grid_scores_ attribute will be soon deprecated. It is better to use the cv_results attribute. It can be implemente in a similar fashion to that of @sascha method: def plot_grid_search(cv_results, grid_param_1, grid_param_2, name_param_1, name_param_2): # Get Test Scores Mean and std for each grid search scores_mean = … Read more
There should be two underscores between estimator name and it’s parameters in a Pipeline logisticregression__C. Do the same for tfidfvectorizer It is mentioned in the user guide here: https://scikit-learn.org/stable/modules/compose.html#nested-parameters. See the example at https://scikit-learn.org/stable/auto_examples/compose/plot_compare_reduction.html#sphx-glr-auto-examples-compose-plot-compare-reduction-py
import joblib joblib.dump(grid.best_estimator_, ‘filename.pkl’) If you want to dump your object into one file – use: joblib.dump(grid.best_estimator_, ‘filename.pkl’, compress = 1)
Cross-validation is when you reserve part of your data to use in evaluating your model. There are different cross-validation methods. The simplest conceptually is to just take 70% (just making up a number here, it doesn’t have to be 70%) of your data and use that for training, and then use the remaining 30% of … Read more