Tag: gridsearchcv

Trying to use GridSearchCV returning error: Check the list of available parameters with `estimator.get_params().keys()`

I’m trying to build a Voting Ensemble model, with a data transformation pipeline. I still need to put the transformation of the response variable into the pipeline. I’m trying to use GridSearchCV to evaluate the best parameters for each algorithm, but when I try to run the last code block, I get an error. But when I run this last

Error while doing SVR for multiple outputs

grid-search gridsearchcv python svm

Trying to do SVR for multiple outputs. Started by hyper-parameter tuning which worked for me. Now I want to create the model using the optimum parameters but I am getting an error. How to fix this? Output: Trying to create a model using the output: Error: Answer Please consult the MultiOutputRegressor docs. The regressor you got back is the model.

How do I make sure GridSearchCV first does the cross split and then the imputing?

data-preprocessing gridsearchcv machine-learning python scikit-learn

I have a GridSearchCV, with a pipeline that looks something like this: my GridSearchCV looks like this: with Cross Validation = 5 So, how do I ensure that I split the data first, and then impute in the most frequent? Answer GridSearchCV will run roughly like this: You can be sure that SimpleImputer and StandardScaler will do .fit() and .transform()

Error with precision_score of XGBoost classifier with RandomizedSearchCV

gridsearchcv machine-learning make-scorer python xgboost

I’m trying to make a classifier with XGBoost, I fit it with RandomizedSearchCV. Here is the code of my function: When I run the code, I get an error, reported below: When I do the same thing but with GridSearchCV instead of RandomizedSearchCV, the code runs without any problems! Answer It’s not precision_score it’s ‘precision_score’ (with ‘ ‘), like this-

RandomizedSearchCV: All estimators failed to fit

gridsearchcv python scikit-learn

I am currently working on the “French Motor Claims Datasets freMTPL2freq” Kaggle competition (https://www.kaggle.com/floser/french-motor-claims-datasets-fremtpl2freq). Unfortunately I get a “NotFittedError: All estimators failed to fit” error whenever I am using RandomizedSearchCV and I cannot figure out why that is. Any help is much appreciated. The first five rows of the original dataframe data_freq look like this: The error I get is

Pipeline with count and tfidf vectorizer produces TypeError: expected string or bytes-like object

gridsearchcv pipeline python scikit-learn tf-idf

I have a corpus like the following ‘C C C 0 0 0 X 0 1 0 0 0 0’, ‘C C C 0 0 0 X 0 1 0 0 0 0’, ‘C C C 0 0 0 X 0 1 0 0 0 0’, ‘X X X’, ‘X X X’, ‘X X X’, I would like to use

GridSearchCV progress in Jupiter Notebook

gridsearchcv jupyter-notebook machine-learning python xgboost

Is it possible to see the progress of GridSearchCV in a Jupyter Notebook? I’m running this script in python: I can see only some warnings in the output of the cell. Answer You want the verbose parameter: An example of what I got on toy data:

pipeline for RandomOversampler, RandomForestClassifier & GridSearchCV

gridsearchcv imblearn python random-forest scikit-learn

I am working on a binary text classification problem. As the classes are highly imbalanced, I am using sampling techniques like RandomOversampler(). Then for classification I would use RandomForestClassifier() whose parameters need to be tuned using GridSearchCV(). I am trying to create a pipeline to do these in order but failed so far. It throws invalid parameters. Answer The parameters