Grid search for decision tree. com/kcoupc/indicadores-tablero-ford-f150-2007.

Jan 22, 2018 · It goes something like this : optimized_GBM. These include regularization parameters, scaling Apr 11, 2018 · 1. Apr 13, 2023 · The original C++ algorithms are designed to build scalable decision tree models that can handle large datasets and high-dimensional feature spaces. Side note: AdaBoost always uses another classifier as a base estimator : it's a 'meta classifier' that works by fitting several version of the 'base Define the argument name and search range as a dictionary. fit(X_train, y_train) What fit does is a bit more involved than usual. GridSearchCV can be given a list of classifiers to choose from for the final step in a pipeline. Once it has the best combination, it runs fit again on all data passed to Jun 3, 2020 · In this post it is mentioned. Mar 25, 2021 · Practically, decision tree is one of the algorithms that can be trained quickly, therefore it’s fine to start with a broad parameter range and a fairly large step size and conduct grid search. T == Average Temperature (°C) TM == Maximum temperature (°C) Tm == Minimum temperature (°C) SLP == Atmospheric pressure at sea level (hPa) Apr 30, 2021 · Stack Exchange Network. If “log2”, then max_features=log2 (n_features). I am trying to use the GridSearchCV to evaluate different models with different parameter sets. By integrating this library into the wider TF ecosystem, users are able now to easily build scalable RF and GBT models without having to learn another language. 1,572 3 3 gold badges 28 28 silver badges 42 42 bronze badges. Then we can zoom in to a sub-range where we think the better values are located and perform another grid search with a smaller step size. Level Up Coding. cv_results_ Add decision trees to your EDA and get great insights from the start. The resume that got a software engineer a $300,000 job at Google. This technique is used when decision tree will have very large depth and will show overfitting of model. Aug 21, 2019 · Phrased as a search problem, you can use different search strategies to find a good and robust parameter or set of parameters for an algorithm on a given problem. Firstly, takes the longer range for the parameters and do the coarse search where you get and idea about approx range that needs to be given for the parameters search. Some model parameters cannot be learned directly from a data set during model training; these kinds of parameters are called hyperparameters. get_metadata_routing [source] # Get metadata routing of this object. This makes your training faster and adds some randomness to your classifier (might also help against overfitting). Max_depth is the Grid Search. e. 8% chance of being worse than '3_poly' . predict_proba(xtest)[:, 1] tree_performance = roc_auc_score(ytest, tree_preds) Q1: once we perform the above steps and get the best parameters, we need to fit a tree with Aug 28, 2020 · Bagged Decision Trees (Bagging) The most important parameter for bagged decision trees is the number of trees (n_estimators). Post Pruning : This technique is used after construction of decision tree. Alexander Nguyen. Here, X is the feature attribute and y is the target attribute (ones we want to predict). Dtree. May 7, 2021 · Random Forest with Grid Search. It is also a good idea to use both random search and grid search to get the best possible results. Supported strategies are “best” to choose the best split and “random” to choose the best random split. model_selection import RandomizedSearchCV # Number of trees in random forest. Grid Search to find the best parameters for decision tree classification. The first is the model that you are optimizing. It is possible that better performance can be achieved with a different class weighting, and this too will depend on the choice of performance metric used to evaluate the model. We can now use Grid Search and Random Search methods to improve our model's performance (test accuracy score). predict() What it will do is, call the StandardScalar () only once, for one call to clf. Choosing min_resources and the number of candidates#. Getting a great model fit. First, it runs the same loop with cross-validation, to find the best parameter combination. We will use air quality data. Since your estimators are Pipeline objects, the best_estimator_ attribute will return a pipeline as well. The class allows you to: Apply a grid search to an array of hyper-parameters, and. model_selection import GridSearchCV def dtree_grid_search(X,y,nfolds): #create a dictionary of all values we want to test param_grid = { 'criterion':['gini','entropy'],'max_depth': np. Note that in the docs you also have suggested values for several Jan 11, 2023 · grid = GridSearchCV(SVC(), param_grid, refit = True, verbose = 3) # fitting the model for grid search. Decision trees are random. Topics random-search decision-tree-algorithm grid-search-hyperparameters Feb 9, 2022 · The GridSearchCV class in Sklearn serves a dual purpose in tuning your model. Decision Tree Regression With Hyper Parameter Tuning. GridSearchCV is from the sklearn library and gives us the ability to grid search our parameters. The value of the hyperparameter has to be set before the learning process begins. Using grid search we were able to tune selected hyperparameters in 247 seconds and increased accuracy to 88%. It's also important to mention that I need to pass a fixed sample_weight parameter to the classifier and that "avgUniqueness" is a int value that controls the number of samples for each tree. For example, c in Support Vector Machines, k in k-Nearest Neighbors, the number of hidden layers in Neural Networks. Two simple and easy search strategies are grid search and random search. They also might have determined that 3 generalizes better on unseen data. tree_. Nov 12, 2021 · But with this solution you can just hyper-tune the classifier rather than the whole ensemble at once. this is a very small grid and there is not much choice anyways, which may explain why accuracy and f1 give you the same parameter combinations and hence the same scoring tables. feature_importances_. Same thing we can do with Logistic Regression by using a set of values of learning rate to find Feb 5, 2019 · If you set maxfeatures to 3 as in your example, your decision tree just looks at three random features and takes the best features of those to make the split. Return the depth of the decision tree. Grid searching is a module that performs parameter tuning which is Apr 16, 2024 · Grid Search. Uses Cross Validation to prevent overfitting. Refresh. Don’t miss the forest for the trees. In scikit-learn, this technique is provided in the GridSearchCV class. So why not just include more values for each parameter? Hyperparameter tuning by randomized-search. where step_name is the corresponding name in your pipeline. There could be a combination of parameters that further improves the performance of the model. best_estimator_['regressor'], # <-- added indexing here. For example, we can apply grid searching on K-Nearest Neighbors by validating its performance on a set of values of K in it. Oct 16, 2022 · One way to combat overfitting is to use a decision tree as part of a ensemble learning model, such as a random forest. 8147086914995224 Now, I want to use these parameters while calling a function that visualizes a decision tree. 4 days ago · In Python, grid search is performed using the scikit-learn library’s sklearn. Returns: self. Grid search is a model hyperparameter optimization technique. # Prepare a hyperparameter candidates. If you are familiar with machine learning, you may have worked with algorithms like Linear Regression, Logistic Regression, Decision Trees, Support Vector Machines, etc. Feb 18, 2023 · How Decision Tree Regression Works – Step By Step. Error: NotFittedError: This XGBRegressor instance is not fitted yet. Aug 21, 2020 · Grid Search Weighted Decision Tree. Sep 12, 2021 · grid search approach to the prediction algorithms. Implementation of Grid Search to find better hyper-parameters for decision tree to reduce the over fitting. . It will trial all combinations and locate the one combination that gives the best results. from sklearn. Sep 30, 2017 · That is a design decision by the sklearn team. , the AUC) is the sum of the green and yellow areas, and the contribution to the score is the height of the areas, so basically only the green one is significant for the score. However is there any way to print the decision-tree based on GridSearchCV. 4. The Python implementation of Grid Search can be done using the Scikit-learn GridSearchCV function. Returns: routing MetadataRequest Explore and run machine learning code with Kaggle Notebooks | Using data from Titanic - Machine Learning from Disaster Apr 12, 2017 · refit=True)) clf. Decision trees are an intuitive supervised machine learning algorithm that allows you to classify data with high degrees of accuracy. It is also Apr 17, 2022 · April 17, 2022. I am trying to use to sklearn grid search to find the optimal parameters for the decision tree. In this tutorial, you’ll learn how to create a decision tree classifier using Sklearn and Python. Aug 13, 2021 · In this Scikit-Learn learn tutorial I've talked about hyperparameter tuning with grid search. When you train (i. [2]. Random search is faster than grid search and should always be used when you have a large parameter space. Data Collection: The first step in creating a decision tree regression model is to collect a dataset containing both input features (also known as predictors) and output values (also called target variable). RandomizedSearchCV implements a “fit” and a “score” method. This has been much easier than trying all parameters by hand. PreliminariesLet's import some common packages:import numpy as np import pandas as pd It also implements “score_samples”, “predict”, “predict_proba”, “decision_function”, “transform” and “inverse_transform” if they are implemented in the estimator used. Since they were trained on smaller sets, these Decision Trees will likely perform worse than the first Decision Tree, achieving only about 80% accuracy. It also implements “score_samples”, “predict”, “predict_proba”, “decision_function”, “transform” and “inverse_transform” if they are implemented in the estimator used. One aspect of using smart meters is detecting anomalies in advanced metering infrastructure. Explore and run machine learning code with Kaggle Notebooks | Using data from multiple data sources May 25, 2019 · I need to plot a heatmap for finding best hyperparameter for decision tree after grid search for donorschoose data set which is available from kaggle. Comparison between grid search and successive halving. The parameters of the estimator used to apply these methods are optimized by cross-validated grid-search over a parameter grid. Grid Search passes all combinations of hyperparameters one by one into the model and check the result. Oct 12, 2020 · We are getting the highest accuracy with the trees that are six levels deep, using 75 % of the features for max_features parameter and using 10 estimators. fit(xtrain, ytrain) tree_preds = tree. arange(3, 15)} # decision tree model dtree_model=DecisionTreeClassifier() #use gridsearch to test all Dec 29, 2018 · 4. n_estimators = [int(x) for x in np. export_graphviz(model. validation), the metric you receive might be biased, because your model overfit to the training data. The first step is to load the dataset: This is a simple multi-class classification dataset for wine recognition. Test Train Data Splitting: The dataset is then divided into two parts: a training set Sep 29, 2023 · The benefits of decision-tree-based models are their insensitivity to missing values, ability to maintain both regular qualities and data, and high efficiency. However, Spark is telling me that DecisionTree currently only supports maxDepth <= 30. Electricity theft, as a well-known anomaly, can be discovered by various machine learning algorithms. Aug 18, 2021 · If you want to see all of the metrics returned by Grid Search, use this code. Sep 29, 2020 · What is Grid Search? Grid search is a technique for tuning hyperparameter that may facilitate build a model and evaluate a model for every combination of algorithms parameters per grid. Like mentioned above, the dataset may be well balanced which is why F1 and accuracy scores may prefer the same parameter combinations. A model hyperparameter is a characteristic of a model that is external to the model and whose value cannot be estimated from data. def Grid_Search_CV_RFR(X_train, y_train): from sklearn. linspace(start = 200, stop = 2000, num = 10)] # Number of features to consider at every split. How many splits can your Decision Tree do? How do we normalize our Linear Regression (if at all!)? To answer these types of questions, we might turn to the Oct 5, 2022 · Use random search on a broad range of values if you don’t already have an idea of the parameters that will perform well on your model. predict(x Aug 1, 2021 · Compare the accuracy score from the first Decision Tree to the accuracy score after you performed the grid search. Jan 5, 2017 · The parameters combination that would give best accuracy is : {'max_depth': 5, 'criterion': 'entropy', 'min_samples_split': 2} The best accuracy achieved after parameter tuning via grid search is : 0. Dec 10, 2020 · 1. Dec 10, 2016 · We’ll stick to a simple decision tree. Some examples of hyperparameters include the number of predictors that are sampled at splits in a tree-based model (we call this mtry in tidymodels) or the learning rate in a boosted tree model (we call this learn_rate). It has the Feb 21, 2019 · I want to create a Decision Tree and do hyperparameter tuning on the parameters and have the model output what the optimal hyperparameters are. arange(3, 10)} tree = GridSearchCV(DecisionTreeClassifier(), param_grid) tree. Dec 28, 2020 · The best combination of parameters found is more of a conditional “best” combination. named_steps ["step_name"]. best_estimator_, out_file=None, filled=True, rounded=True, feature_names=X_train. target. splitter: string, optional (default=”best”) The strategy used to choose the split at each node. fit() instead of multiple calls as you described. Evaluate these 1,000 Decision Trees on the test set. You'll be able to find the optimal set of hyperparameters for a May 21, 2020 · The combinatorial grid search is the best way to navigate these new questions and find the best combination of hyperparameters and parameters for our model and it’s data. 99. This tutorial won’t go into the details of k-fold cross validation. Decision-tree-based techniques, as compared to other ML algorithms, are better for short-term forecasting and may have a faster computation speed . max_depth=5, Sep 18, 2020 · Specifically, it provides the RandomizedSearchCV for random search and GridSearchCV for grid search. 1. best_estimator_. Oct 20, 2021 · Photo by Roberta Sorge on Unsplash. model_selection. Good values might be a log scale from 10 to 1,000. tree import DecisionTreeClassifier from sklearn. in. pipe = Pipeline(steps=[. Jun 24, 2021 · Grid Layouts. In this post, we will go through Decision Tree model building. When constructing this class, you must provide a dictionary of hyperparameters to evaluate in the param_grid argument. Introduction. 2. It won't do exactly what you have in your code though: most notably, the fitted models do not get saved by GridSearchCV, just the scores (and the finally chosen refit-on-all-data model, if refit != False ). Jun 7, 2021 · Decision tree models generally tend to overfit. fit) your model on some data, and then calculate your metric on that same training data (i. Decision trees are constructed by recursively partitioning the data based on the values of features until a stopping criterion is met. Call 'fit' with appropriate arguments before using this estimator. Both classes require two arguments. But why max_depth=3? The developers probably determine this by considering a default value that is applicable to most use-cases. The above picture represents how Grid and Randomized Grid Search might perform trying to optimize a model which scoring function (e. Two of the key challenges in machine learning are finding the right algorithm to use and optimizing your model. Mar 20, 2024 · Decision trees are powerful models extensively used in machine learning for classification and regression tasks. The parameters of the estimator used to apply these methods are optimized by cross-validated grid-search over a Jun 1, 2018 · Once you’ve got the modeling basics down, you should have a reasonable grasp on what tool to use in what instance. Beside factor, the two main parameters that influence the behaviour of a successive halving search are the min_resources parameter, and the number of candidates (or parameter combinations) that are evaluated. 8% chance of being worse than 'linear', and a 1. Dec 28, 2021 · 0. 5 as the optimal out of the 3 values tested, it would be computationally expensive to test all mincriterion values from 0. Scikit-learn provides these two methods for algorithm parameter tuning and examples of each are provided below. Train one Decision Tree on each subset, using the best hyperparameter values found above. SyntaxError: Unexpected token < in JSON at position 4. param_grid = {'max_depth': np. Now you can use a grid search object to make new predictions using the best parameters. feature_importance() if you happen ran this through a Pipeline and receive object has no attribute 'feature_importance' try optimized_GBM. Explore and run machine learning code with Kaggle Notebooks | Using data from Titanic - Machine Learning from Disaster. Several standard metrics are used to assess the method’s efficiency, like accuracy, precision, recall, f1-score, AUC-ROC, MAE, RMSE, R 2 and Jul 2, 2021 · By default, the ADA Boost Model uses a Decision Tree with a max depth of 1 as its weak learner. 💚 A heart disease classifier using 4 SVM kernels and decision trees, with PCA, ROC, pruning, grid search cv, confusion matrix, and more Topics svm rbf-kernel pca pruning confusion-matrix roc decision-tree decision-tree-classifier svm-classifier linear-kernel one-hot-encoding grid-search-cross-validation cost-complexity-pruning poly-kernel Jan 19, 2023 · Hyper-parameters of Decision Tree model. 字典，键为参数名，值为可选的参数 Nov 11, 2019 · Each criterion is superior in some cases and inferior in others, as the “No Free Lunch” theorem suggests. This is due to the fact that the search can only test the parameters that you fed into param_grid. fit() clf. Parameters like in decision criterion, max_depth, min_sample Nov 28, 2023 · from sklearn. All machine learning algorithms have a range of hyperparameters which effect how they build the model. There are a number of parameters that can be grid searches in an ADA Boost Model but the Decision Tree as a base estimator is one of those parameters: dtc = DecisionTreeClassifier() ada = ADABoostClassifier(base_estimator = dtc) Jun 17, 2021 · 2. But after that step, the difference between a good model and a great model lies in the way you implement that solution. In this paper, decision tree, random forest, and gradient boosting methods are implemented and performed on collected power consumption data from 114 single-family apartments to detect non-technical May 24, 2021 · To implement the grid search, we used the scikit-learn library and the GridSearchCV class. Model Optimization with GridSearchCV. Here I have two hyperparameters: max_depth=[ Randomized search on hyper parameters. 3 Support Vector Regression (SVR) Cross validation is a technique to calculate a generalizable metric, in this case, R^2. Initializing a decision tree classifier with max_depth=2 and fitting our feature Aug 23, 2023 · A decision tree is a tree-like structure where each internal node represents a feature or attribute, each branch represents a decision rule, and each leaf node represents an outcome or a class label. Our goal was to train a computer vision model that can automatically recognize the texture of an object in an image (brick, marble, or sand). Feb 13, 2021 · I didn't. However, a grid-search approach has limitations. . 3. 1. The training pipeline itself included: Looping over all images in our dataset. You won't get the same best_estimator_ every time you re-run. Cross-validate your model using k-fold cross validation. All parameters in the grid search that don't start with base_estimator__ are Adaboost's, and the others are 'forwarded' to the object we pass as base_estimator argument (DTC in the sample). columns) dot_data. You have to further access the correct step with your regressor by indexing it, for example: plot_tree(. Python Implementation of Grid Search. Defining parameter grid: We defined a dictionary named param_grid, where the keys are hyperparameters of the decision tree classifier such as criterion, max_depth, min_samples_split, and min_samples_leaf. The depth of a tree is the maximum distance between the root and any leaf. 01 to 0. grid. Mar 13. tree import DecisionTreeClassifier. 'rbf' and 'linear' have a 43% probability of being practically equivalent, while 'rbf' and '3_poly' have a 10% chance of being so. To get the best set of hyperparameters we can use Grid Search. Initial random forest classifier with default hyperparameter values reached 81% accuracy on the test. If “sqrt”, then max_features=sqrt (n_features). The majority of machine learning models contain parameters that can be adjusted to vary how the model learns. Jun 22, 2019 · 上一篇介绍了决策树Sklean库的参数，今天用GridSearchCV来进行调参，寻找到最优的参数一、GridSearchCV介绍① estimator: 训练器，可以是分类或是回归，这里就用决策树分类和决策树回归② param_grid: 调整的参数，可以有两种方式:a. Oct 1, 2015 · Btw. Add a Aug 19, 2022 · 3. Parameters like in decision criterion, max_depth, min_sample_split, etc. Explore and run machine learning code with Kaggle Notebooks | Using data from Car Evaluation Data Set If the issue persists, it's likely a problem on our side. Secondly, do the grid search but this time with fine search by selecting the range from the intuitions and approximation got from first step. The parameters of the estimator used to apply Jul 1, 2015 · Here is the code for decision tree Grid Search. Follow asked Dec 5, 2017 at 18:06. Ideally, this should be increased until no further improvement is seen in the model. GridSearchCV function. Edit: Changed refit to True, when GridSearchCV is used inside a pipeline. n_estimators in [10, 100, 1000] For the full list of hyperparameters, see: An optimal model can then be selected from the various different attempts, using any relevant metrics. Dtree= DecisionTreeRegressor() parameter_space = {'max_features Jan 9, 2018 · To use RandomizedSearchCV, we first need to create a parameter grid to sample from during fitting: from sklearn. Read more in the User Guide. Decision tree example Say we want to run a simple decision tree to predict cars’ transmission type (am) based on their miles per gallon (mpg) and horsepower (hp) using the mtcars data set. It also implements “predict”, “predict_proba”, “decision_function”, “transform” and “inverse_transform” if they are implemented in the estimator used. In the previous notebook, we showed how to use a grid-search approach to search for the best hyperparameters maximizing the generalization performance of a predictive model. Grid Search. Is that what you had expected? We perform a round of grid searching in order to elucidate the optimal hyperparameter values. However, the performance of decision trees highly relies on the hyperparameters, selecting the optimal hyperparameter can sign Oct 19, 2018 · It is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset. GridSearchCV implements a “fit” and a “score” method. Three of the most popular approaches for hyperparameter tuning include Grid Search, Randomised Search, and Bayesian Search. I am using PySpark for machine learning and I want to train decision tree classifier, random forest and gradient boosted trees. Successive Halving Iterations. Coming from a Python background, GridSearchCV was very straightforward and does exactly this. Explore and run machine learning code with Kaggle Notebooks | Using data from Heart Disease Prediction. Stack Exchange network consists of 183 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. Doing so allows you to identify the optimal hyperparameter values to be used for training your model. May 5, 2020 · One solution is taking the best parameters from gridsearchCV and then form a decision tree with those parameters and plot the tree. Examples. Image by Yoshua Bengio et al. As mentioned in documentation: refit : boolean, default=True Refit the best estimator with the entire dataset. The maximum depth of the tree. If the issue persists, it's likely a problem on our side. model_selection import GridSearchCV from sklearn. decision-tree; grid-search; Share. Looking at the documentation, I am Sep 29, 2021 · In this article, we used a random forest classifier to predict “type of glass” using 9 different attributes. I’ve deliberately chosen input variables and hyperparameters that highlight the approach. Let us say the dimension of your data is 50 and the max_feature is 10, each time you need to find the split, you randomly select 10 features and use them to decide which one of the 10 is the best feature to use. max_depth int. Please check User Guide on how the routing mechanism works. To understand how grid search works with decision trees classifier, let’s take a look at an example. keyboard_arrow_up. #. The function looks something like this Apr 24, 2017 · I want to improve the parameters of this GridSearchCV for a Random Forest Regressor. max_depth = np. Mar 21, 2024 · Grid Searching can be applied to any hyperparameters algorithm whose performance can be improved by tuning hyperparameter. data[:, 2 :] y =iris. Using a class weighting that is the inverse ratio of the training data is just a heuristic. Implements Standard Scaler function on the dataset. How do we pick the best value for C? The best value is dependent on the Nov 18, 2019 · Decision Tree’s are an excellent way to classify classes, unlike a Random forest they are a transparent or a whitebox classifier which means we can actually find the logic behind decision tree Aug 4, 2022 · How to Use Grid Search in scikit-learn. Well Results show that the model ranked first by GridSearchCV 'rbf', has approximately a 6. Both techniques evaluate models for a given hyperparameter vector using cross-validation, hence the “ CV ” suffix of each class name. Jun 20. It operates by combining K-Fold Cross-Validation with a grid of parameters Apr 15, 2020 · If “auto”, then max_features=sqrt (n_features). There are several different techniques for accomplishing this task. Performs train_test_split on your dataset. 1-page. Max_feature is the number of features to consider each time to make the split decision. Initializing the X and Y parameters and loading our dataset: iris = load_iris() X = iris. 3. arange(1, 10) params = {'max_depth':max_depth} Next, we define an instance of the grid search, where we pass the decision-tree-model instance and the above dictionary. g. How does it differ? It is most likely that you will find the accuracy score has decreased. I want to try out different maximum depth values and select the best one via grid search and cross-validation. So in general I'd suggest you carefully look at what each of them does, and follow suggestions from reliable resources. Aug 22, 2019 · The caret R package provides a grid search where it or you can specify the parameters to try on your problem. Here, we will work with the sklearn’s wine dataset to look into tuning hyperparameters for our model. content_copy. The examples in this post will demonstrate how you can use the caret R package to tune a machine learning algorithm. Grid search is a technique for tuning hyperparameter that may facilitate build a model and evaluate a model for every combination of algorithms parameters per grid. Let’s discover the implementation of how the hyperparameter gets tuned in decision trees with the help of grid search. After doing this, I would like to fit the model using these parameters. Here is the link to data. wasd wasd. 2. First, we’ll try Grid Search. The structure of decision trees resembles the flowchart of decisions helps us to interpret and explain easily. Say we want to tune the decision tree hyperparameters max_depth and min_samples_leaf for the Iris dataset. In this tutorial, you’ll learn how the algorithm works, how to choose different parameters for Jun 4, 2020 · Approach 1: dot_data = tree. It does not scale well when the number of parameters to tune increases. We might use 10 fold cross-validation to search the best value for that tuning hyperparameter. What I mean is that because your first model showed mincriterion=0. Decision Tree Performance, ML. In other words, cross-validation seeks to Mar 9, 2020 · b. grid_search_rfc = grid_clf_acc. For example, the logistic regression model, from sklearn, has a parameter C that controls regularization,which affects the complexity of the model. This is a map of the model parameter name and an array May 22, 2022 · DirectionsThe main purpose of this assignment is for you to gain experience creating and visualizing a Decision Tree along with sweeping a problem's parameter space - in this case by performing a grid search. Feb 10, 2019 · Grid search parameters for Decision Tree. Unexpected token < in JSON at position 4. Logistic Regression and k-NN do not cause a problem but Decision Tree, Random Forest and some of the other types of classifiers do not work when n_jobs=-1. aw vd td ea vx eg fe ts jd lu