Cross validation random forest python. model_selection import cross_validate iris = datasets.

" GitHub is where people build software. Jul 12, 2024 · It might increase or reduce the quality of the model. Apr 10, 2019 · If you perform a grid search within cross-validation, then you will have multiple sets of hyperparameters, each of which did the best on their grid-search validation sub-subset of the cross-validation split. e. predict(X_test) May 26, 2020 · Example of a 5-fold cross-validation data split. I need to train different Random Forests, each with a different number of trees. In this case, among 10-fold cross-validation and random sampling, Use 10-fold cross-validation. The class is configured with the number of folds (splits), then the split () function is called, passing in the dataset. trim_5_df = DataFrame(columns=features_to_use) run=1. com. if rf. The first thing we do is importing Aug 26, 2020 · The main parameters are the number of folds ( n_splits ), which is the “ k ” in k-fold cross-validation, and the number of repeats ( n_repeats ). (or, random sampling many times) Calculate mean accuracy of each fold. Randomly divide a dataset into k groups, or “folds”, of roughly equal size. honest=true. To associate your repository with the cross-validation topic, visit your repo's landing page and select "manage topics. See full list on machinelearningmastery. When routing is enabled, pass groups alongside other metadata via the params argument instead. cross-validation scores #. This process is repeated multiple times, each time using a different Nov 26, 2021 · $\begingroup$ K-Fold cross-validation is not a training methodology, it is actually a model selection methodology. Feb 28, 2017 · You have to create an instance of a random forest classifier like this: clf = RandomForestClassifier() Then you need to load featuresets (I don't have this data so I couldn't test my code) and convert your categorical variable into a numerical one, for example through a dictionary: May 18, 2018 · The random forest is an ensemble learning method, composed of multiple decision trees. Building upon the foundational knowledge in Section 3, this section guides participants through the practical implementation of the Random Forest algorithm. data y = iris. I love random forests. This module dives into machine learning algorithms, specifically Random Forest, to predict events based on a set of attributes. loss. For example assuming you have a grid dict, named "grid", and RF model object, named "rf", then you can do something like this: rf. I am running the Random forest and have a question: below is my code: i ran this code 5-10 and every time the output is almost same with +-3 and to overcome this problem ( running manually each time) we can use the CV function. # First create the base model to tune. It is widely used for classification and regression predictive modeling problems with structured (tabular) data sets, e. From the plot above one can further notice a plateau of equivalent scores (similar mean value and overlapping errorbars) for 3 to 5 selected features. First I use hyperparameter tuning to tune my model, Secondly, I perform a manual cross-valiadation split using KFold=10 to train the data, Finally, I choose the best model from the cross-validation training data with the highest score. Apr 2, 2019 · However, I could not find how to perform feature importance for cross validation in sklearn. It involves splitting the dataset into k subsets or folds, where each fold is used as the validation set in turn while the remaining k-1 folds are used for training. 2. Apr 21, 2016 · The Bootstrap Aggregation algorithm for creating multiple different models from a single training dataset. Let’s first import all the objects we need, that are our dataset, the Random Forest regressor and the object that will perform the RFE with CV. The XGBoost library allows the models to be trained in a way that repurposes and harnesses the computational efficiencies implemented in the library for training random forest models. My dataset is already split in 10 different subsets, so I'd like to use them to do k-fold cross validation, without using automatic functions that randomly split the dataset. cv int, cross-validation generator or an iterable, default=None. For eg if you want to choose between Decision Tree and Naive Bayes models, you will run K-Fold cross-validation to check how much accuracy you might expect using both of these models. Explore and run machine learning code with Kaggle Notebooks | Using data from Gender Recognition by Voice. The thing is that I can see that the "cv" parameter of RandomizedSearchCV is used to do the cross validation. . The most frequently used evaluation metric of survival models is the concordance index (c index, c statistic). An ensemble method is a technique that combines the predictions from multiple machine learning. fit(X,y) fails: the cross-validator creates subsets of X and y, sub_X and sub_y and Apr 11, 2022 · Many models are available: random forest, XGBoost, support vector machines, logistic regression, etc. Otherwise, you can use the code block below, to calculate the F1 score at each fold using the testing data and validation data. Stacking occurs in the following steps: Split the data into a training and validation set. from statistics import mean, stdev. Oct 28, 2017 · 11. Dec 21, 2023 · Cross validation is a technique used in machine learning to evaluate the performance of a model on unseen data. It is defined as the ratio of correctly ordered Aug 1, 2017 · To implement the random forest algorithm we are going follow the below two phase with step by step workflow. Jun 12, 2023 · Grid Search Cross-Validation. Sep 30, 2016 · The mistake you are making is calling the RandomForestClassifier whose default arg, random_state is None. Build a model using only data from the training set. But the Monte Carlo CV will have a higher bias than the K-fold CV. with fixed time intervals), in train/test sets. 交差検証（Cross-validation）による汎化性能の評価. Existen múltiples implementaciones de modelos Random Forest en Python, siendo una de las más utilizadas es la disponible en scikit-learn. 1. answered Jun 6, 2015 at 21:59. Creating dataset. com Distributed Random Forest (DRF) is a powerful classification and regression tool. グリッドサーチ（grid search）と呼ば If the issue persists, it's likely a problem on our side. honest_fixed_separation: For honest trees only i. This pipeline is not a ‘Scikit-Learn’ pipeline, but ‘imblearn’ pipeline. Random Forest can also be used for time series forecasting, although it requires that the Aug 21, 2018 · I am trying to implement a Random Forest classifier using both stratifiedKFold and RandomizedSearchCV. Python3. g. toc: true. The random forest is a powerful machine learning model, but that should not prevent us from knowing how it works. keyboard_arrow_up. Splitting data into train and test datasets. If scoring represents a single score, one can use: Mar 25, 2022 · You can't use 'cross_val_score' or 'cross_val_predict' to get back a model post-cross-validation. A hyperparameter grid in the form of a Python dictionary with names and values of parameter names must be passed as Nov 16, 2023 · Cross Validation with Scikit-Learn. fit(X_train, y_train) pred = random_forest. May 7, 2015 · Just to add one more point to keep it clear. # empty dataframe. In the above code snippet, we’ve used SMOTE as a part of a pipeline. recall médio do random forest: 13,0%. Feb 4, 2021 · I would like to understand how to optimize the algorithm quality in generalization starting from cross-validation technique. In this article, we will explore the implementation of K-Fold Cross-Validation using Scikit-Learn, a popular Python Nov 19, 2021 · The k-fold cross-validation procedure is available in the scikit-learn Python machine learning library via the KFold class. Jul 14, 2020 · Cross-validation is considered the gold standard when it comes to validating model performance and is almost always used when tuning model hyper-parameters. I save the model and then I test the Jan 6, 2016 · I think the easiest way is to create your grid of parameters via ParameterGrid() and then just loop through every set of params. Reduce least important feature and repeat. But I do not understand how is this possible. Method 2. KFold(n_splits=10, random_state=42) model=RandomForestClassifier(n_estimators=50) I got the results of the 10 folds Nov 28, 2014 · I have a training set and a cross-validation set. ensemble import RandomForestRegressor #STEP3 : define a simple Random Forest model attirbutes model May 8, 2021 · What I basically want to do is do a 10-fold cross validation on the RF model. Grid Search Cross-Validation is a popular tuning technique that chooses the best set of hyperparameters for a model by iterating and evaluating through all possible combinations of given parameters. Train a base model on k-1 folds and make predictions on the k-th fold. However, if i use the cross_val_score function the results are very different ( i ran the this function for 10 Jun 11, 2020 · Random Forest is an ensemble technique which can be used for both regression and classification tasks. stackexchange. The first part details the algorithm that we will use today in part two to plot Jul 10, 2015 · 6. # this will remove the 5 worst features determined by their feature importance computed by the RF classifier. For regression, it is up to you, it can be mean squared errors, a. Strategy to evaluate the performance of the cross-validated model on the test set. from sklearn. linspace(start = 200, stop = 2000, num = 10)] # Number of features to consider at every split. Random Forest en Python. I have multiclass (class 1, 2, , 10) I would like to know how to apply 10-fold cross-validation instead of train_test_split. Calculate the test MSE on the observations in the fold Apr 27, 2021 · Random forest is a simpler algorithm than gradient boosting. set_params(**g) rf. In this article, we will explore the implementation of K-Fold Cross-Validation using Scikit-Learn, a popular Python machine- Jun 12, 2017 · # STEP1 : split my_data into [predictors] and [targets] predictors = my_data[[ 'variable1', 'variable2', 'variable3' ]] targets = my_data. According to the documentation: the results of cross_val_score is Array of scores of the estimator for each run of the cross validation. E. The first is the model that you are optimizing. A good default for k is k=10. More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects. There are Mar 22, 2022 · TLDR: Is a tree in a random forest regressor using GridSearchCV build from a number of samples equal to the size of the entire training data (n_train) or from a number of samples equal to the size of the subset of the training data used for training (in this case n_train/k * (k-1) = with k = 2 gives 500)? Dec 16, 2019 · Therefore, in your particular use-case, you should use: GridSearchCV, SelectFromModel, and cross_val_score: RandomForestRegressor(n_jobs=-1), threshold="mean". The concept of learning by partitioning is not only simple and intuitive, it’s also easy to Sep 18, 2023 · Stacking ensemble learning. In this paper, Honest trees are trained with the Random Forest algorithm with a sampling without replacement. Benefit 2: Robust process Sep 1, 2020 · This is the second part in the series on leave-one-person-out cross validation with random forests in Python. One of the most commonly used cross-validation techniques is K-Fold Cross-Validation. This dilemma is common in machine learning and is called the Bias-Variance tradeoff. improve the model to its optimal settings, and 2. Plot number of features VS. I want to only divide the Amsterdam data into 10-fold, then I want to add the rest of the large_city dataset (so all neighbourhoods except those in Amsterdam) to the training sets of all fold, but leave the test folds the same. Nov 4, 2020 · One commonly used method for doing this is known as k-fold cross-validation , which uses the following approach: 1. from sklearn import preprocessing. Since we don’t know which one will work best, we try a few of them. , by using an average importance score) in the 10-folds of cross validation. model_selection import cross_validate iris = datasets. ensemble import RandomForestRegressor. The default of random forest in R is to have the maximum depth of the trees, so that is ok. Import packages. cross_val_score: This function is used to perform k-fold cross-validation to evaluate the performance of a model; Step-1: Import Libraries . Really, I love all decision tree–based models. load_iris() X = iris. In most common cross-validation approach you use part of the training set for testing. Subsequently I would perform a cross-validation in order to estimate the performance of the model and the prediction using the test set. Each of these trees is a weak learner built on a subset of rows and columns. As such, the procedure is often called k-fold cross-validation. We already have results for the Random Forest so let’s now run cross-validation on the Support Vector Machine. A good default for the number of repeats depends on how noisy the estimate of model performance is on the dataset. Watch out though, this array is global, so make sure you don't write to it in a way you can't interpret the results. Indeed, the optimal model selected by the RFE can lie within this range, depending on Explore and run machine learning code with Kaggle Notebooks | Using data from Home Credit Default Risk. Machine Learning with Random Forest and Cross Validation. sufficiently report the results. You should instead check things like the importance metrics of your RF model. Oct 7, 2019 · A random forest model generally does not require cross validation. Nov 4, 2020 · One commonly used method for doing this is known as leave-one-out cross-validation (LOOCV), which uses the following approach: 1. How's this possible? Im using random forest regression algorithm to predict some data. Jul 20, 2015 at 15:53. model_selection module provides us with KFold class which makes it easier to implement cross-validation. from sklearn import model_selection. So let’s say we try Random Forest and Support Vector Machine. In this section we will use cross validation to evaluate the performance of Random Forest Algorithm for classification. Cutting to the chase they found that Random Forest (specifically parallel random forest in R) and Gaussian Support Vector Machines (specifically from libSVM) performed the best overall. : cross_val_predict(, params={'groups': groups}). Aug 30, 2018 · A random forest reduces the variance of a single decision tree leading to better predictions on new data. So, it picks up the seed generated by np. I did this: but i didn't know how to fit it. In most cases, K-fold CV will be good enough and is computationally less Dec 23, 2017 · 1. oob_score_ > best_score: best_score Jul 31, 2019 · I am a beginner in machine learning. target_variable # STEP2 : import the required libraries from sklearn import cross_validation from sklearn. Build Phase. By default, from my understanding, it is the accuracy of your classifier on each fold. This is my code, I've tidied it up a bit to make it relevant to your task: features_to_use = fea_cols # this is a list of features. The results of the split () function are enumerated to give the row indexes for the train and test A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. This is the Summary of lecture "Model Validation in Python", via datacamp. Because the Fitbit sleep data set is relatively small, I am going to use 4-fold Cross-Validation and compare the three models used so far: Multiple Linear Regression, Random Forest and Extreme Gradient Boosting Regressor. Oct 6, 2017 · I have an imbalanced dataset containing a binary classification problem. Default: False. – David. KFold(n_splits=8) Jul 21, 2015 · Jul 20, 2015 at 15:18. In this tutorial, you will discover how to use the XGBoost library to develop random forest ensembles. Section 4: Random Forest Algorithm Implementation. Jan 10, 2023 · Stratified k-fold cross-validation is the same as just k-fold cross-validation, But Stratified k-fold cross-validation, it does stratified sampling instead of random sampling. Add this topic to your repo. Jun 2, 2016 · 10. from sklearn import metrics. rfcv=RandomForestRegressor() cv = model_selection. I have built Random Forest Classifier and used k-fold cross-validation with 10 folds. model_selection import cross_val_score val = cross_val_score(estimator=classifier, X=X_train, y=y_train, cv=5) Know to fil the model with cross validation shall i do Mar 10, 2021 · I am using a Random Forest Classifier and I want to perform k-fold cross validation. ensemble import RandomForestClassifier from sklearn. classifier= RandomForestClassifier(n_estimators=100, random_state=0) from sklearn. Implementation: Step 1: Import the required libraries. Nov 12, 2020 · sklearn. The details of the dataset are available at the following link: May 15, 2024 · I want to perform a random forest model using cross validation technique. Dec 27, 2017 · Additionally, if we are using a different model, say a support vector machine, we could use the random forest feature importances as a kind of feature selection method. This process is repeated k times, and Jul 29, 2019 · 本記事は pythonではじめる機械学習の 5 章（モデルの評価と改良）に記載されている内容を簡単にまとめたものになっています．. Both classes require two arguments. a. Apr 28, 2019 · I want to cross validate a random forest model. Jul 2, 2024 · K-fold cross validation in machine learning cross-validation is a powerful technique for evaluating predictive models in data science. Repeat until you have a prediction for each fold. The random_state in both StratifiedKFold and RandomForestClassifier need to be the same inorder to produce equal arrays of scores of cross validation. 具体的には，python3 の scikit-learn を用いて. We’ll go through the process step by step. Jun 6, 2015 · return precision_score(y_true, y_pred, **kwargs) scorer = make_scorer(score_func) Then use scoring=scorer in your cross-validation. Mais uma evidência de que a classificação do modelo de regressão logística tem maior capacidade de apontar os funcionários com maior risco ou maior When routing is enabled, pass groups alongside other metadata via the params argument instead. It might be worth investing some time into seeing whether logistic regression/a neural network perform as well as a random forest w. In cross-validation, the process divides the train data further into two parts – the train data and Mar 29, 2021 · Let’s look at the right way to use SMOTE while using cross-validation. , with k=3 folds, K-fold cross validation will generate 3 (training, test) dataset pairs, each of which uses 2/3 of the data for training and 1/3 for testing. K-fold cross validation performs model selection by splitting the dataset into a set of non-overlapping randomly partitioned folds which are used as separate training and test datasets e. In other words, cross-validation seeks to Aug 6, 2020 · K-fold Cross-Validation in Python. estimator which gave highest score (or smallest loss if specified) on the left out data. I think you sholud ask that question on statistician SO: stats. Sep 3, 2018 · Having obtained my data and committed to a random forest regression, I'm looking to understand what further tests I need to do in order to; 1. It involves dividing the available data into multiple folds or subsets, using one of these folds as a validation set, and training the model on the remaining folds. Operational Phase. r. : cross_validate(, params={'groups': groups}). model_selection import RandomizedSearchCV # Number of trees in random forest. When given a set of data, DRF generates a forest of classification or regression trees, rather than a single classification or regression tree. validation), the metric you receive might be biased, because your model overfit to the training data. A value of 3, 5, or 10 repeats is probably a good Evaluating Survival Models #. Refresh. This is the result of introducing correlated features. Code: Python code implementation of Stratified K-Fold Cross-Validation. The cross-validation has a single hyperparameter “ k ” that controls the number of subsets that a dataset is split into. Cross-validation is primarily used in applied machine learning to estimate the skill of a machine learning model on unseen data. Step 4: Fit Random forest regressor to the dataset. This chapter focuses on performing cross-validation to validate model performance. This post was written for developers and assumes no background in statistics or mathematics. Now that the theory is clear, let’s apply it in Python using sklearn. We focus on testing the algorithm on the SONAR dataset, providing hands-on experience in applying the learned concepts. Oct 11, 2021 · Feature selection in Python using Random Forest. Dec 14, 2018 · and my code for the RandomizedSearchCV like this: # Use the random grid to search for best hyperparameters. I would now use these parameters for my random forest regressor. Hopefully this article has given you the confidence and understanding needed to start using the random forest on your projects. In summary, I want to identify the most effective features (e. – Marcin. Much of the data we come across is clustered, e. in each split, test indices must be higher than before, and thus shuffling Mar 3, 2023 · Cross-validation involves repeatedly splitting data into training and testing sets to evaluate the performance of a machine-learning model. from sklearn import datasets from sklearn. The motivation for writing this package came from the models we have been building at Manifold. Mar 29, 2022 · If you haven’t heard of K nearest neighbor, don’t freak out, you can still learn K-fold CV. The post focuses on how the algorithm Jan 10, 2018 · To use RandomizedSearchCV, we first need to create a parameter grid to sample from during fitting: from sklearn. KFold class has split method which requires a dataset to perform cross-validation on as an input argument. Oct 21, 2017 · For the code below, my r-squared score is coming out to be negative but my accuracies score using k-fold cross validation is coming out to be 92%. n_estimators = [int(x) for x in np. From the abstract of the paper: Aug 29, 2022 · cv=KFold(n_splits=5, shuffle=True, random_state=1)) grid_search. Here we are importing all the necessary libraries required. random to produce the random output. Since, SMOTE doesn’t have a ‘fit_transform’ method, we cannot use it with ‘Scikit-Learn’ pipeline. kfold = model_selection. The problem that we are going to solve is to predict the quality of wine based on 12 attributes. The process of out of bag sampling implicitly cross validates the forest as it being built. feature_selector, RandomForestRegressor(n_jobs=-1) # define the grid of the random-forest for the feature selection. Random Forests are less likely to overfit the other ML algorithms, but cross-validation (or some alternatively hold-out form of evaluation) should still be recommended. Determines the cross-validation splitting strategy. More trees will reduce the variance. Unexpected token < in JSON at position 4. Feb 1, 2017 · To pass the weights when using the grid parameter, the usage is: fit_params={"sample_weight"=weights}) The problem is that the cross-validator isn't aware of sample weights and so doesn't resample them together with the the actual data, so calling grid_search. Both techniques evaluate models for a given hyperparameter vector using cross-validation, hence the “ CV ” suffix of each class name. When you train (i. Aunque es menos conocido, las principales librerías de Gradient Boosting como LightGBM y XGBoost también pueden configurarse para crear modelos Random Forest. data as it looks in a spreadsheet or database table. I did: from sklearn. Step 2: Import and print the dataset. For each forest, I need to plot the classification score for the training set and the cross-validation set (a validation curve). If true, a new random separation is generated for each Oct 5, 2022 · Below is a step-by-step sample implementation of Random Forest Regression. It is a measure of rank correlation between predicted risk scores \ (\hat {f}\) and observed time points \ (y\) that is closely related to Kendall’s τ. By averaging out the impact of several decision trees, random forests tend to improve prediction. I am happy to provide more details if needed. t whatever loss condition you're using under cross-validation. Cross validation is a technique to calculate a generalizable metric, in this case, R^2. Welcome to cross validated! It will be up to you to say what you want the cross validation results for. Max_depth = 500 does not have to be too much. Let’s quickly make a random forest with only the two most important variables, the max temperature 1 day prior and the historical average and see how the performance compares. Training random forest classifier with Python scikit learn. Mar 25, 2018 · 1. If you have interests, you can go through Jul 9, 2024 · In GridSearchCV, along with Grid Search, cross-validation is also performed. Illustration: About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features NFL Sunday Ticket Press Copyright Jan 11, 2016 · I ran Recursive Feature Elimination (RFE) of python sklearn, so I could get the list of 'feature importance ranking'. See "Generalized Random Forests", Athey et al. fit(X,y) # save if best. Handling missing values. python machine-learning random-forest numpy scikit-learn sklearn pandas python3 matplotlib support-vector-machine k-means kfold-cross-validation trend-prediction stock-trend-prediction Updated Sep 15, 2020 . fit) your model on some data, and then calculate your metric on that same training data (i. content_copy. You cannot combine these sets into a single coherent hyperparameter specification, and therefore you cannot deploy your model. Sep 18, 2020 · Specifically, it provides the RandomizedSearchCV for random search and GridSearchCV for grid search. target clf = RandomForestClassifier(n_estimators=10, random_state = 42, class_weight="balanced") cv_results = cross May 9, 2023 · Image generated by DALL·E 2. You do it several times so that each data point appears once in the test set. Jul 18, 2020 · So, in order to actually fit your model and get predictions on your test set (assuming of course that you are satisfied by the actual score returned by cross_val_score ), you need to proceed in doing so as: random_forest. This blog post introduces an open source Python package for implementing mixed effects random forests (MERFs). Share. Choose one of the folds to be the holdout set. 3. Cross-Validation is used while training the model. We performed a binary classification using Logistic regression as our model and cross-validated it using 5-Fold cross-validation. Perform predictions. For this example, I’ll use the Boston dataset, which is a regression dataset. Fit the model on the remaining k-1 folds. Step 3: Select all rows and column 1 from dataset to x and all rows and column 2 as y. Split a dataset into a training set and a testing set, using all but one observation as part of the training set. Divide the training set into K folds. I have the dataset without normalization but I will use StandardScaler in process. There is also the TimeSeriesSplit function in sklearn, which splits time-series data (i. If both perform worse than a random forest, you can weigh up whether the ability to differentiate is a worthwhile trade-off for lower accuracy. Jan 24, 2021 · The Monte Carlo method can give you more confidence in your results and is more repeatable since the variance is low. fit(x_train, y_train) This would give me the best parameters. This can be used for offer users specific recommendations based on their information or it can be used to assess the most important product features that leads Uma outra forma de comparar os modelos em cross-validation é pela média de recall de todos os modelos: recall médio da regressão logística: 46,2%. Cross-validation, or k-fold cross-validation, is a procedure used to estimate the performance of a machine learning algorithm when making predictions on data not used during the training of the model. Possible inputs for cv are: None, to use the default 5-fold cross validation, integer, to specify the number of folds in a (Stratified)KFold, CV splitter, An iterable yielding (train, test) splits as arrays of indices. You should validate your final parameter settings via cross-validation (you then have a nested cross-validation), then you could see if there was some problem in the tuning process. The Random Forest algorithm that makes a small tweak to Bagging and results in a very powerful classifier. As we know that before training the model with data, we divide the data into two parts – train data and test data. You should find the recall values in the recall_accumulator array. Nov 1, 2020 · Random Forest is a popular and effective ensemble machine learning algorithm. Dec 6, 2023 · RandomForestRegressor: This class is used to train a random forest regression model. Jul 31, 2020 · Some algorithms were tuned before contributing their final score and algorithms were evaluated using a 4-fold cross validation. Note that unlike standard cross-validation methods, successive training sets are supersets of those that come before them, i. May 27, 2024 · Cross-validation involves repeatedly splitting data into training and testing sets to evaluate the performance of a machine-learning model. rf = RandomForestRegressor() # Random search of parameters, using 3 fold cross validation, # search across 100 different combinations, and use all Jul 26, 2020 · LOOCV Model Evaluation. When a specific value for k is chosen, it may be used in place of k in the reference to the model, such as k=10 becoming 10-fold cross-validation. longitudinal data from individuals, data clustered by demographics, etc. Jun 27, 2019 · In case, you want to use the CV model for a unseen data point/s, use the following approach. The document says the following: best_estimator_ : estimator or dict: Estimator that was chosen by the search, i. SyntaxError: Unexpected token < in JSON at position 4. k. Possible inputs for cv are: An iterable that generates (train, test) splits as arrays of indices. scoringstr, callable, list, tuple, or dict, default=None. dl sy an rn pa yg tm mo cq mp