cross-validation

I've fit a Pipeline object with RandomizedSearchCV pipe_sgd = Pipeline([('scl', StandardScaler()), ('clf', SGDClassifier(n_jobs=-1))]) param_dist_sgd = {'clf__loss': ['log'], 'clf__penalty': [None, 'l1', 'l2', 'elasticnet'], 'clf__alpha': np.linspace(0.15, 0.35), 'clf__n_iter': [3, 5, 7]} sgd_randomized_pipe = RandomizedSearchCV(estimator = pipe_sgd, param_distributions=param_dist_sgd, cv=3, n_iter=30, n_jobs=-1) sgd_randomized_pipe.fit(X_train, y_train) I want to access the coef_ attribute of the best_estimator_ but I'm unable to do that. I've tried accessing coef_ with the code below. sgd

I have carefully read the CARET documentation at: http://caret.r-forge.r-project.org/training.html , the vignettes, and everything is quite clear (the examples on the website help a lot!), but I am still a confused about the relationship between two arguments to trainControl : method index and the interplay between trainControl and the data splitting functions in caret (e.g. createDataPartition , createResample , createFolds and createMultiFolds ) To better frame my questions, let me use the following example from the documentation: data(BloodBrain) set.seed(1) tmp <- createDataPartition

I am using cross_validation from sklearn, from sklearn.cross_validation import train_test_split I get the below warning: cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Problem: The deprecation warning means that the module is deprecated, i.e. no longer supported. You are using a version for which sklearn.cross_validation is not a module any longer. Solution: from sklearn.model_selection import train_test_split C/O: This post. To avoid this, you just need

I am trying to split a dataset for cross validation and GridSearch in sklearn. I want to define my own split but GridSearch only takes the built in cross-validation methods. However, I can't use the built in cross validation method because I need certain groups of examples to be in the same fold. So, if I have examples: [A1, A2, A3, A4, A5, B1, B2, B3, C1, C2, C3, C4, .... , Z1, Z2, Z3] I want to perform cross validation such that examples from each group [A,B,C...] only exist in one fold. ie K1 contains [D,E,G,J,K...], K2 contains [A,C,L,M,...], K3 contains [B,F,I,...] etc This type of thing

I am fairly new to sci-kit learn and have been trying to hyper-paramater tune XGBoost. My aim is to use early stopping and grid search to tune the model parameters and use early stopping to control the number of trees and avoid overfitting. As I am using cross validation for the grid search, I was hoping to also use cross-validation in the early stopping criteria. The code I have so far looks like this: import numpy as np import pandas as pd from sklearn import model_selection import xgboost as xgb #Import training and test data train = pd.read_csv("train.csv").fillna(value=-999.0) test = pd

I'm working on a project that would show the potential influence a group of events have on an outcome. I'm using the glmnet() package, specifically using the Poisson feature. Here's my code: # de <- data imported from sql connection x <- model.matrix(~.,data = de[,2:7]) y <- (de[,1]) reg <- cv.glmnet(x,y, family = "poisson", alpha = 1) reg1 <- glmnet(x,y, family = "poisson", alpha = 1) **Co <- coef(?reg or reg1?,s=???)** summ <- summary(Co) c <- data.frame(Name= rownames(Co)[summ$i], Lambda= summ$x) c2 <- c[with(c, order(-Lambda)), ] The beginning imports a large amount of data from my