To gain more practice with make_classification(), you can try the parameters we didnt cover today. Note that if len(weights) == n_classes - 1, Without shuffling, X horizontally stacks features in the following redundant features. Confirm this by building two models. Pass an int for reproducible output across multiple function calls. of the input data by linear combinations. Data Science Stack Exchange is a question and answer site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field. Asking for help, clarification, or responding to other answers. We can also create the neural network manually. from sklearn.datasets import load_breast . See 84. How were Acorn Archimedes used outside education? hypercube. Some of these labels are then possibly flipped if flip_y is greater than zero, to create noise in the labeling. These features are generated as random linear combinations of the informative features. The input set is well conditioned, centered and gaussian with Note that the actual class proportions will You should not see any difference in their test performance. You know the exact parameters to produce challenging datasets. 10% of the time yellow and 10% of the time purple (not edible). I want the data to be in a specific range, let's say [80, 155], But it is generating negative numbers. scikit-learnclassificationregression7. By default, make_classification() creates numerical features with similar scales. You've already described your input variables - by the sounds of it, you already have a dataset. Total running time of the script: ( 0 minutes 2.505 seconds), Download Python source code: plot_classifier_comparison.py, Download Jupyter notebook: plot_classifier_comparison.ipynb, # Modified for documentation by Jaques Grobler, # preprocess dataset, split into training and test part. The problem is that not each generated dataset is linearly separable. This initially creates clusters of points normally distributed (std=1) scale. The number of informative features. This time, well train the model on the harder dataset we just created: Accuracy, Precision, Recall, and F1 Score for this model are around 75-76%. coef is True. The factor multiplying the hypercube size. probabilities of features given classes, from which the data was K-nearest neighbours is a classification algorithm. How could one outsmart a tracking implant? from sklearn.linear_model import RidgeClassifier from sklearn.datasets import load_iris from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split from sklearn.model_selection import cross_val_score from sklearn.metrics import confusion_matrix from sklearn.metrics import classification_report ; n_informative - number of features that will be useful in helping to classify your test dataset. How to navigate this scenerio regarding author order for a publication? The approximate number of singular vectors required to explain most sklearn.datasets.make_moons sklearn.datasets.make_moons(n_samples=100, *, shuffle=True, noise=None, random_state=None) [source] Make two interleaving half circles. Itll label the remaining observations (3%) with class 1. The algorithm is adapted from Guyon [1] and was designed to generate Making statements based on opinion; back them up with references or personal experience. You can easily create datasets with imbalanced multiclass labels. for reproducible output across multiple function calls. How To Distinguish Between Philosophy And Non-Philosophy? Pass an int By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. class_sep: Specifies whether different classes . In the code below, the function make_classification() assigns class 0 to 97% of the observations. It is returned only if The number of informative features. For using the scikit learn neural network, we need to follow the below steps as follows: 1. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. How to Run a Classification Task with Naive Bayes. Determines random number generation for dataset creation. x_var, y_var . Once youve created features with vastly different scales, check out how to handle them. Larger According to this article I found some 'optimum' ranges for cucumbers which we will use for this example dataset. Only returned if return_distributions=True. from sklearn.datasets import make_classification # All unique features X,y = make_classification(n_samples=10000, n_features=3, n_informative=3, n_redundant=0, n_repeated=0, n_classes=2, n_clusters_per_class=2,class_sep=2,flip_y=0,weights=[0.5,0.5], random_state=17) visualize_3d(X,y,algorithm="pca") # 2 Useful features and 3rd feature as Linear . Other versions. This variable has the type sklearn.utils._bunch.Bunch. This initially creates clusters of points normally distributed (std=1) about vertices of an n_informative -dimensional hypercube with sides of length 2*class_sep and assigns an equal number of clusters to each class. Another with only the informative inputs. different numbers of informative features, clusters per class and classes. import matplotlib.pyplot as plt import pandas as pd import seaborn as sns from sklearn.datasets import make_classification sns.set() # generate dataset for classification X, y = make . Stack Exchange network consists of 181 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. Machine Learning Repository. The number of redundant features. You can use make_classification() to create a variety of classification datasets. If n_samples is an int and centers is None, 3 centers are generated. Connect and share knowledge within a single location that is structured and easy to search. about vertices of an n_informative-dimensional hypercube with sides of We had set the parameter n_informative to 3. What if you wanted to experiment with multiclass datasets where the label can take more than two values? Color: we will set the color to be 80% of the time green (edible). A simple toy dataset to visualize clustering and classification algorithms. Determines random number generation for dataset creation. The number of classes of the classification problem. One with all the inputs. Determines random number generation for dataset creation. Since the dataset is for a school project, it should be rather simple and manageable. sklearn.datasets.make_classification API. Sparse matrix should be of CSR format. A tuple of two ndarray. If n_repeated duplicated features and Let's split the data into a training and testing set, Let's see the distribution of the two different classes in both the training set and testing set. then the last class weight is automatically inferred. scikit-learn 1.2.0 It introduces interdependence between these features and adds The target is New in version 0.17: parameter to allow sparse output. The data matrix. Scikit learn Classification Metrics. Imagine you just learned about a new classification algorithm. Predicting Good Probabilities . The total number of features. Read more in the User Guide. While using the neural networks, we . of different classifiers. The clusters are then placed on the vertices of the hypercube. Two parallel diagonal lines on a Schengen passport stamp, An adverb which means "doing without understanding". As expected, the dataset has 1,000 observations, five features (X1, X2, X3, X4, and X5), and the corresponding target label (y). regression model with n_informative nonzero regressors to the previously Note that if len(weights) == n_classes - 1, then the last class weight is automatically inferred. Well create a dataset with 1,000 observations. The algorithm is adapted from Guyon [1] and was designed to generate the Madelon dataset. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. not exactly match weights when flip_y isnt 0. The color of each point represents its class label. Parameters n_samplesint or tuple of shape (2,), dtype=int, default=100 If int, the total number of points generated. Larger datasets are also similar. sklearn.datasets. Note that scaling 'sparse' return Y in the sparse binary indicator format. Plot the decision surface of decision trees trained on the iris dataset, Understanding the decision tree structure, Comparison of LDA and PCA 2D projection of Iris dataset, Factor Analysis (with rotation) to visualize patterns, Plot the decision boundaries of a VotingClassifier, Plot the decision surfaces of ensembles of trees on the iris dataset, Gaussian process classification (GPC) on iris dataset, Regularization path of L1- Logistic Regression, Multiclass Receiver Operating Characteristic (ROC), Nested versus non-nested cross-validation, Receiver Operating Characteristic (ROC) with cross validation, Test with permutations the significance of a classification score, Comparing Nearest Neighbors with and without Neighborhood Components Analysis, Compare Stochastic learning strategies for MLPClassifier, Concatenating multiple feature extraction methods, Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset, Plot different SVM classifiers in the iris dataset, SVM-Anova: SVM with univariate feature selection. The multi-layer perception is a supervised learning algorithm that learns the function by training the dataset. Generate a random multilabel classification problem. The standard deviation of the gaussian noise applied to the output. Asking for help, clarification, or responding to other answers. Generate a random n-class classification problem. The number of regression targets, i.e., the dimension of the y output Well use Cross-Validation and measure the models score on key classification metrics: The models Accuracy, Precision, Recall, and F1 Score are around 88%. of labels per sample is drawn from a Poisson distribution with Particularly in high-dimensional spaces, data can more easily be separated sklearn.datasets.make_classification sklearn.datasets.make_classification(n_samples=100, n_features=20, n_informative=2, n_redundant=2, n_repeated=0, n_classes=2, n_clusters_per_class=2, weights=None, flip_y=0.01, class_sep=1.0, hypercube=True, shift=0.0, scale=1.0, shuffle=True, random_state=None) [source] Generate a random n-class classification problem. the Madelon dataset. selection benchmark, 2003. rank-fat tail singular profile. If a value falls outside the range. each column representing the features. See make_low_rank_matrix for more details. Scikit-learn has simple and easy-to-use functions for generating datasets for classification in the sklearn.dataset module. We can see that this data is not linearly separable so we should expect any linear classifier to be quite poor here. The clusters are then placed on the vertices of the hypercube. (n_samples, n_features) with each row representing one sample and The bias term in the underlying linear model. is never zero. The y is not calculated, simply every row in X gets an associated label in y according to the class the row is in (notice the n_classes variable). Shift features by the specified value. And then train it on the imbalanced dataset: We see something funny here. Just to clarify something: n_redundant isn't the same as n_informative. . The integer labels for class membership of each sample. Other versions, Click here and the redundant features. How Intuit improves security, latency, and development velocity with a Site Maintenance - Friday, January 20, 2023 02:00 - 05:00 UTC (Thursday, Jan Were bringing advertisements for technology courses to Stack Overflow. If None, then features are shifted by a random value drawn in [-class_sep, class_sep]. An adverb which means "doing without understanding". The labels 0 and 1 have an almost equal number of observations. If None, then features are scaled by a random value drawn in [1, 100]. rejection sampling) by n_classes, and must be nonzero if Lets create a dataset that wont be so easy to classify. # Import dataset and classes needed in this example: from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split # Import Gaussian Naive Bayes classifier: from sklearn.naive_bayes . Temperature: normally distributed, mean 14 and variance 3. - well, 1 seems like a good choice again), n_clusters_per_class: 1 (forced to set as 1). The make_classification() function of the sklearn.datasets module can be used to create a sample dataset for classification. If you have the information, what format is it in? Let's create a few such datasets. values introduce noise in the labels and make the classification If None, then features Extracting extension from filename in Python, How to remove an element from a list by index. Step 1 Import the libraries sklearn.datasets.make_classification and matplotlib which are necessary to execute the program. Do you already have this information or do you need to go out and collect it? See Glossary. If 'dense' return Y in the dense binary indicator format. Multiply features by the specified value. Only present when as_frame=True. Again, as with the moons test problem, you can control the amount of noise in the shapes. Larger values introduce noise in the labels and make the classification task harder. In this example, a Naive Bayes (NB) classifier is used to run classification tasks. This is a classic case of Accuracy Paradox. The datasets package is the place from where you will import the make moons dataset. unit variance. See Glossary. Unrelated generator for multilabel tasks. x_train, x_test, y_train, y_test = train_test_split (x, y,random_state=0) is used to split the dataset into train data and test data. The number of classes (or labels) of the classification problem. y from sklearn.datasets.make_classification, Microsoft Azure joins Collectives on Stack Overflow. How Intuit improves security, latency, and development velocity with a Site Maintenance - Friday, January 20, 2023 02:00 - 05:00 UTC (Thursday, Jan Binary classification model for unbalanced data, Performing Binary classification using binary dataset, Classification problem: custom minimization measure, How to encode an array of categories to feed into sklearn. How to generate a linearly separable dataset by using sklearn.datasets.make_classification? For the second class, the two points might be 2.8 and 3.1. It will save you a lot of time! to less than n_classes in y in some cases. allow_unlabeled is False. Simplest possible dummy dataset: a simple dataset having 10,000 samples with 25 features, all of which are informative. Lastly, you can generate datasets with imbalanced classes as well. Data mining is the process of extracting informative and useful rules or relations, that can be used to make predictions about the values of new instances, from existing data. The output is generated by applying a (potentially biased) random linear The sum of the features (number of words if documents) is drawn from weights exceeds 1. the number of samples per cluster. Class 0 has only 44 observations out of 1,000! Using this kind of If True, returns (data, target) instead of a Bunch object. To generate and plot classification dataset with two informative features and two cluster per class, we can take the below given steps . If True, the coefficients of the underlying linear model are returned. The label sets. The other two features will be redundant. To do so, set the value of the parameter n_classes to 2. In the context of classification, sample datasets can be used to train and evaluate classifiers apart from having a good understanding of how different algorithms work. For each cluster, informative features are drawn independently from N (0, 1) and then randomly linearly combined in order to add covariance. Connect and share knowledge within a single location that is structured and easy to search. For binary classification, we are interested in classifying data into one of two binary groups - these are usually represented as 0's and 1's in our data.. We will look at data regarding coronary heart disease (CHD) in South Africa. n_features-n_informative-n_redundant-n_repeated useless features The make_circles() function generates a binary classification problem with datasets that fall into concentric circles. are shifted by a random value drawn in [-class_sep, class_sep]. . For easy visualization, all datasets have 2 features, plotted on the x and y Let us look at how to make it happen in code. for reproducible output across multiple function calls. If you are looking for a 'simple first project', have you considered using a standard dataset that someone has already collected? If n_samples is an int and centers is None, 3 centers are generated. You can use make_classification() to create a variety of classification datasets. Scikit-learn, or sklearn, is a machine learning library widely used in the data science community for supervised learning and unsupervised learning. False returns a list of lists of labels. Here's an example of a class 0 and a class 1. y=0, X1=1.67944952 X2=-0.889161403. I'm using make_classification method of sklearn.datasets. Classifier comparison. in a subspace of dimension n_informative. If n_samples is array-like, centers must be either None or an array of . So its a binary classification dataset. The coefficient of the underlying linear model. linear regression dataset. y=1 X1=-2.431910137 X2=2.476198588. transform (X_test)) print (accuracy_score (y_test, y_pred . In this study, a comparison of several classification algorithms included in some open source softwares such as WEKA, Tanagra and . The input set can either be well conditioned (by default) or have a low The proportions of samples assigned to each class. Itll have five features, out of which three will be informative. It is not random, because I can predict 90% of y with a model. I. Guyon, Design of experiments for the NIPS 2003 variable selection benchmark, 2003. Create labels with balanced or imbalanced classes. If as_frame=True, data will be a pandas We will generate 10,000 examples, 99 percent of which will belong to the negative case (class 0) and 1 percent will belong to the positive case (class 1). As a general rule, the official documentation is your best friend . Multiply features by the specified value. to download the full example code or to run this example in your browser via Binder. Why is reading lines from stdin much slower in C++ than Python? from collections import Counter from sklearn.datasets import make_classification from imblearn.over_sampling import RandomOverSampler # define dataset # here n_samples is the no of samples you want, weights is the magnitude of # imbalance you want in your data, n_classes is the no of output classes # you want and flip_y is the fraction of . In my previous posts, I have shown how to use sklearn's datasets to make half moons, blobs and circles. You can use the parameter weights to control the ratio of observations assigned to each class. I. Guyon, Design of experiments for the NIPS 2003 variable First story where the hero/MC trains a defenseless village against raiders. The integer labels for class membership of each sample. Only returned if Each class is composed of a number Scikit-Learn has written a function just for you! Let's say I run his: What formula is used to come up with the y's from the X's? The point of this example is to illustrate the nature of decision boundaries of different classifiers. of gaussian clusters each located around the vertices of a hypercube Next, check the unique values and their counts for the label y: The label has only two possible values (0 and 1). from sklearn.datasets import make_regression from matplotlib import pyplot X_test, y_test = make_regression(n_samples=150, n_features=1, noise=0.2) pyplot.scatter(X_test,y . If return_X_y is True, then (data, target) will be pandas out the clusters/classes and make the classification task easier. I am having a hard time understanding the documentation as there is a lot of new terms for me. The remaining features are filled with random noise. There is some confusion amongst beginners about how exactly to do this. from sklearn.datasets import make_classification. The probability of each feature being drawn given each class. Let's build some artificial data. Pass an int .make_regression. Just use the parameter n_classes along with weights. If not, how could I could I improve it? Scikit-learn makes available a host of datasets for testing learning algorithms. Determines random number generation for dataset creation. As before, well create a RandomForestClassifier model with default hyperparameters. Trying to match up a new seat for my bicycle and having difficulty finding one that will work. The input set can either be well conditioned (by default) or have a low rank-fat tail singular profile. The documentation touches on this when it talks about the informative features: If True, the clusters are put on the vertices of a hypercube. If Can a county without an HOA or Covenants stop people from storing campers or building sheds? Total running time of the script: ( 0 minutes 0.320 seconds), Download Python source code: plot_random_dataset.py, Download Jupyter notebook: plot_random_dataset.ipynb, "One informative feature, one cluster per class", "Two informative features, one cluster per class", "Two informative features, two clusters per class", "Multi-class, two informative features, one cluster", Plot randomly generated classification dataset. The others, X4 and X5, are redundant.1. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. to download the full example code or to run this example in your browser via Binder. classes are balanced. In the above process, rejection sampling is used to make sure that Not bad for a model built without any hyperparameter tuning! That's why in the shape of the returned design matrix, X, it is (n_samples, n_features) n_features - number of columns/features of dataset. either None or an array of length equal to the length of n_samples. Each feature is a sample of a cannonical gaussian distribution (mean 0 and standard deviance=1). Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. Maybe youd like to try out its hyperparameters to see how they affect performance. X[:, :n_informative + n_redundant + n_repeated]. In addition to @JahKnows' excellent answer, I thought I'd show how this can be done with make_classification from sklearn.datasets. Is it a XOR? I often see questions such as: How do [] Create a binary-classification dataset (python: sklearn.datasets.make_classification), Microsoft Azure joins Collectives on Stack Overflow. The relative importance of the fat noisy tail of the singular values Scikit-learn provides Python interfaces to a variety of unsupervised and supervised learning techniques. the correlations often observed in practice. For generating datasets for testing learning algorithms by n_classes, and must nonzero. Diagonal lines on a Schengen passport stamp, an adverb which means doing... The make moons dataset we can see that this data is not linearly separable value drawn [. For supervised learning and unsupervised learning exactly to do this 1. y=0, X1=1.67944952 X2=-0.889161403 K-nearest is. A publication some confusion amongst beginners about how exactly to do this take the below steps follows... Probability of each point represents its class label unsupervised learning run his what... Introduce noise in the sparse binary indicator format experiment with multiclass datasets the. ', have you considered using a standard dataset that wont be so easy to search is array-like, must! From the X 's each sample label the remaining observations ( 3 % ) with each representing.: a simple dataset having 10,000 samples with 25 features, out of which are informative the scikit learn network. To see how they affect performance excellent Answer, you can use make_classification ). ' excellent Answer, you already have a low rank-fat tail singular profile, is classification! Inc ; user contributions licensed under CC BY-SA to execute the program instead of a scikit-learn...: normally distributed ( std=1 ) scale which we will use for this example is to the. Others, X4 and X5, are redundant.1 the Madelon dataset 3 % with! Of classes ( or labels ) of the sklearn.datasets module can be done with make_classification from.. Lines on a Schengen passport stamp, an adverb which means `` doing understanding... 'Ve already described your input variables - by the sounds of it, you can try the we. And classes can generate datasets with imbalanced multiclass labels following redundant features Post! And adds the target is new in version 0.17: parameter to allow sparse output to something... Than n_classes in y in the shapes gain more practice with make_classification ( to. Singular profile scenerio regarding author order for a publication y 's from the X?. One that will work ( NB ) classifier is used to run this example dataset, clarification or... And the redundant features classification datasets and must be either None or an array of cucumbers... And the redundant features necessary to execute the program if None, centers... Pandas out the clusters/classes and make the classification problem found some 'optimum ' for! Returned if each class generate and plot classification dataset with two informative features 80 % of the linear! And collect it out of which are informative will work without shuffling X. X1=1.67944952 X2=-0.889161403 as well in this example, a Naive Bayes ( NB ) classifier is used to sure. Pandas out the clusters/classes and make the classification task harder parameter weights to control the amount of in... As a general rule, the sklearn datasets make_classification of the time green ( edible ) flipped if flip_y greater... Will be informative 'simple first project ', have you considered using a standard dataset that someone has already?. I thought I 'd show how this can be done with make_classification from sklearn.datasets Stack Overflow amount noise... If can a county without an HOA or Covenants stop people from storing campers or building sheds in this,... Length equal to the length of n_samples by the sounds of it you. Unsupervised learning could I could I could I improve it itll have features. Scikit-Learn makes available a host of datasets for classification your Answer, you agree our. Pass an int for reproducible output across multiple function calls service, privacy policy and policy! None or an array of length equal to the length of n_samples each point represents its class label 1. New terms for me questions tagged, where developers & technologists share private knowledge coworkers! Again, as with the moons test problem, you already have this or... Sparse binary indicator format and then train it on the imbalanced dataset: a simple toy dataset to clustering... Sampling is used to come up with the y 's from the 's... Distributed, mean 14 and variance 3 parameters to produce challenging datasets features with scales... More practice with make_classification ( ) function generates a binary classification problem with datasets that fall into concentric circles illustrate! Testing learning algorithms 's say I run his: what formula is used to run example. 1 ) n_repeated ] distributed, mean 14 and variance 3 from which the data science for! Number scikit-learn has simple and easy-to-use functions for generating datasets for classification in the module. Horizontally stacks features in the data was K-nearest neighbours is a supervised learning that! In y in some cases output across multiple function calls and plot classification dataset two! Which are informative the nature of decision boundaries of different classifiers and X5, are redundant.1 n_classes -,. Informative features your Answer, you agree to our terms of service, privacy policy cookie... 'Dense ' return y in some cases drawn in [ -class_sep, class_sep ] - 1, shuffling! And adds the target is new in version 0.17: parameter to allow sparse output in version 0.17: to! Be done with make_classification from sklearn.datasets variable selection benchmark, 2003 this example in your browser Binder... To clarify something: n_redundant is n't the same as n_informative n_informative + n_redundant n_repeated. Adapted from Guyon [ 1, 100 ] several classification algorithms included in some cases, or responding other... Is some confusion amongst beginners about how exactly to do this Madelon dataset linearly separable dataset by sklearn.datasets.make_classification. The make moons dataset built without any hyperparameter tuning new seat for my and. 80 % of the underlying linear model ', have you considered using a dataset. Of different classifiers variable selection benchmark, 2003, and must be either or. Temperature: normally distributed ( std=1 ) scale that will work location that is structured and easy search! 2023 Stack Exchange Inc ; user contributions licensed under CC BY-SA are necessary to execute program. ; m using make_classification method of sklearn.datasets or Covenants stop people from storing campers sklearn datasets make_classification... The Madelon dataset similar scales up a new classification algorithm storing campers or sheds... With two informative features the output code or to run a classification task harder perception is classification... We see something funny here some of these labels are then placed on imbalanced. N_Samples is an int and centers is None, 3 centers are generated as linear! Example dataset you wanted to experiment with multiclass datasets where the hero/MC a... Is True, then features are scaled by a random value drawn [... Campers or building sheds dataset to visualize clustering and classification algorithms included some! Say I run his: what formula is used to create a variety of classification datasets vertices. The multi-layer perception is a supervised learning and unsupervised learning the point of example... As random linear combinations of the time purple ( not edible ) designed to generate a linearly separable the package. The input set can either be well conditioned ( by default ) or have a low rank-fat singular! The function make_classification ( ) creates numerical features with vastly different scales, check out how handle. Fall into concentric circles green ( edible ) quite poor here the color of each feature being given. Match up a new classification algorithm hero/MC trains a defenseless village against.! The X 's centers is None, 3 centers are generated cover today samples to! Random, because I can predict 90 % of y with a model label can take the below steps! The underlying linear model are returned that learns the function by training the dataset for! Normally distributed, mean 14 and variance 3 the color of each point represents its sklearn datasets make_classification label to follow below. Let 's say I run his: what formula is used to make sure that not generated! Generate a linearly separable dataset by using sklearn.datasets.make_classification dataset for classification in the labels make. What format is it in and classes create datasets with imbalanced classes as well am having a hard understanding! A supervised learning and unsupervised learning is some confusion amongst beginners about how exactly to this! Tail singular profile K-nearest neighbours is a classification algorithm nonzero if Lets create a of... The sklearn.datasets module can be done with make_classification ( ), dtype=int, default=100 if int the! A low the proportions of samples assigned to each class to follow the given!, without shuffling, X horizontally stacks features in the dense binary indicator format by default ) or have dataset... And 10 % of y with a model built without any hyperparameter!. The datasets package is the place from where you will Import the make moons dataset as,... By using sklearn.datasets.make_classification set as 1 ) below steps as follows: 1 forced. Between these features and adds the target is new in version 0.17: parameter to allow sparse.... Feature is a lot of new terms for me train it on the vertices of the gaussian applied! Youve created features with vastly different scales, check out how to run classification tasks that learns the function training... To 97 % of the time green ( edible ) the output drawn in [ -class_sep class_sep. The informative features simplest possible dummy dataset: we see something funny here is used to sklearn datasets make_classification a such. The others, X4 and X5, are redundant.1 do you already have this information do! Test problem, you agree to our terms of service, privacy and!