grid-search

In sklearn, a serial pipeline can be defined to get the best combination of hyperparameters for all consecutive parts of the pipeline. A serial pipeline can be implemented as follows: from sklearn.svm import SVC from sklearn import decomposition, datasets from sklearn.pipeline import Pipeline from sklearn.model_selection import GridSearchCV digits = datasets.load_digits() X_train = digits.data y_train = digits.target #Use Principal Component Analysis to reduce dimensionality # and improve generalization pca = decomposition.PCA() # Use a linear SVC svm = SVC() # Combine PCA and SVC to a

I am working in scikit and I am trying to tune my XGBoost. I made an attempt to use a nested cross-validation using the pipeline for the rescaling of the training folds (to avoid data leakage and overfitting) and in parallel with GridSearchCV for param tuning and cross_val_score to get the roc_auc score at the end. from imblearn.pipeline import Pipeline from sklearn.model_selection import RepeatedKFold from sklearn.model_selection import GridSearchCV from sklearn.model_selection import cross_val_score from xgboost import XGBClassifier std_scaling = StandardScaler() algo = XGBClassifier() steps

I'm running multiple nested loops to do hyper parameter grid search. Each nested loop runs through a list of hyper parameter values and inside the innermost loop, a Keras sequential model is built and evaluated each time using a generator. (I'm not doing any training, I'm just randomly initializing and then evaluating the model multiple times and then retrieving the average loss). My problem is that during this process, Keras seems to be filling up my GPU memory, so that I eventually get an OOM error. Does anybody know how to solve this and free up the GPU memory each time after a model is

currently I have successfully defined a custom kernel function(pre-computing the kernel matrix) using def function, and now I am using the GridSearchCV function to get the best parameters. so, in the custom kernel function, there is a total of 2 parameters which will be tuned (Namely gamm and sea_gamma in the example below), and also, for SVR model, the cost c parameter has to be tuned as well. But until now, I can just tune the cost c parameter using GridSearchCV -> please refer to the Part I: example below. I have searched for some similar solutions such as: Is it possible to tune parameters

I'm using scickit-learn to tune a model hyper-parameters. I'm using a pipeline to have chain the preprocessing with the estimator. A simple version of my problem would look like this: import numpy as np from sklearn.model_selection import GridSearchCV from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression grid = GridSearchCV(make_pipeline(StandardScaler(), LogisticRegression()), param_grid={'logisticregression__C': [0.1, 10.]}, cv=2, refit=False) _ = grid.fit(X=np.random.rand(10, 3), y=np.random.randint(2,