grid-search

I'm trying to use the log_loss argument in the scoring parameter of GridSearchCV to tune this multi-class (6 classes) classifier. I don't understand how to give it a label parameter. Even if I gave it sklearn.metrics.log_loss , it would change for each iteration in the cross-validation so I don't understand how to give it the labels parameter? I'm using Python v3.6 and Scikit-Learn v0.18.1 How can I use GridSearchCV with log_loss with multi-class model tuning? My class representation: 1 31 2 18 3 28 4 19 5 17 6 22 Name: encoding, dtype: int64 My code: param_test = {"criterion": ["friedman_mse"

I currently have following script that helps to find the best model for a doc2vec model. It works like this: First train a few models based on given parameters and then test against a classifier. Finally, it outputs the best model and classifier (I hope). Data Example data (data.csv) can be downloaded here: https://pastebin.com/takYp6T8 Note that the data has a structure that should make an ideal classifier with 1.0 accuracy. Script import sys import os from time import time from operator import itemgetter import pickle import pandas as pd import numpy as np from argparse import ArgumentParser

I was checking sklearn documentation webpage about GridSearchCV . One of attributes of GridSearchCV object is best_estimator_ . So here is my question. How to pass more than one estimator to GSCV object? Using a dictionary like: {'SVC()':{'C':10, 'gamma':0.01}, ' DecTreeClass()':{....}} ? GridSearchCV works on parameters. It will train multiple estimators (but same class (one of SVC, or DecisionTreeClassifier, or other classifiers) with different parameter combinations from specified in param_grid . best_estimator_ is the estimator which performs best on the data. So essentially best_estimator

I have a question about the cv parameter of sklearn's GridSearchCV . I'm working with data that has a time component to it, so I don't think random shuffling within KFold cross-validation seems sensible. Instead, I want to explicitly specify cutoffs for training, validation, and test data within a GridSearchCV . Can I do this? To better illuminate the question, here's how I would to that manually. import numpy as np import pandas as pd from sklearn.linear_model import Ridge np.random.seed(444) index = pd.date_range('2014', periods=60, freq='M') X, y = make_regression(n_samples=60, n_features=3

I am new to scikit, and have 2 slight issues to combine a data scale and grid search. Efficient scaler Considering a cross validation using Kfolds, I would like that each time we train the model on the K-1 folds, the data scaler (using preprocessing.StandardScaler() for instance) is fit only on the K-1 folds and then apply to the remaining fold. My impression is that the following code, will fit the scaler on the entire dataset, and therefore I would like to modify it to behave as described previsouly: classifier = svm.SVC(C=1) clf = make_pipeline(preprocessing.StandardScaler(), classifier)