metrics import precision_score precision_score(y_true, y_pred, labels=[0,1,2], average='weighted') Output: 0. metrics import pairwise_distances from sklearn import datasets dataset = datasets. core import Dense, Activation from keras. 1 documentation. Normalized Mutual Information (NMI) is a normalization of the Mutual Information (MI) score to scale the results between 0 (no mutual information) and 1 (perfect correlation). metrics' 3 plot_confusion_matrix() got an unexpected keyword argument 'classes' using sklearn Compute the laplacian kernel between X and Y. But it is always preferred to split the data. confusion_matrix¶ sklearn. fit(X) labels = kmeans_model. If None, uses Y=X. If float, should be between 0. Normalize but am struggling to get something to work since ConfusionMatrixDisplay is a sklearn object that creates a different than usual matplotlib plot. top_k_accuracy_score(y_true, y_score, *, k=2, normalize=True, sample_weight=None, labels=None) [source] #. Now that the metrics of a classification problem are under our belt. ("Normalized confusion matrix", 'true')] disp = plot_confusion_matrix(logreg, X_test, y_test, The Iris Dataset. 6. This is a scoring function to be used Gallery examples: Features in Histogram Gradient Boosting Trees Lagged features for time series forecasting Feb 26, 2018 · Outputs a boolean matrix. pairwise_distances. Parameters: y_true 1d array-like, or label indicator array / sparse matrix. metrics'. Axes, optional) – The axes upon which to plot the curve See full list on jcchouinard. metrics import accuracy_score, confusion_matrix accuracy_score(my_class_column, my_forest_train_prediction) confusion_matrix(my_test_data, my_prediction_test_forest) Also the probability for each prediction can be added: my_classifier_forest. layers. if 'all', the confusion matrix is normalized by the total number of samples; if None (default), the confusion matrix will not be normalized. Jan 21, 2020 · 10. If you are trying to use it with a custom dataset, you will need to convert the dataset to a format that is supported by `sklearn. pair_confusion_matrix (labels_true, labels_pred) [source] # Pair confusion matrix arising from two clusterings. Parameters: X{array-like, sparse matrix} of shape (n_samples_X, n_features) A feature array. Scikit-learn Implementation . This function computes Cohen’s kappa , a score that expresses the level of agreement between two annotators on a classification problem. recall_score (y_true, y_pred, *, labels = None, pos_label = 1, average = 'binary', sample_weight = None, zero_division = 'warn') [source] # Compute the recall. ensemble import RandomForestClassifier np. balanced_accuracy_score(y_true, y_pred, *, sample_weight=None, adjusted=False) [source] #. Feb 15, 2020 · Following the scikit-learn's documentation, I spotted this parameter called values_format, but I do not know how to manipulate this parameter so that it can suppress the scientific notation. It is recommend to use from_estimator or from_predictions to create a RocCurveDisplay. contingency_matrix(y_true, y_pred) # Find optimal one-to-one mapping between cluster labels and true labels row_ind Normalizes confusion matrix over the true (rows), predicted (columns) conditions or all the population. Compute confusion matrix to evaluate the accuracy of a classification. predict_proba(variable 1, variable n) The DistanceMetric class provides a convenient way to compute pairwise distances between samples. Added in version 0. This data sets consists of 3 different types of irises’ (Setosa, Versicolour, and Virginica) petal and sepal length, stored in a 150x4 numpy. The Haversine (or great circle) distance is the angular distance between two points on the surface of a sphere. This example uses a Tf-idf-weighted document-term sparse matrix to encode the If metric is a string, it must be one of the options allowed by sklearn. In the particular case when y_true is constant, the explained variance score is not finite: it is either NaN (perfect predictions) or -Inf (imperfect predictions). cohen_kappa_score (y1, y2, *, labels = None, weights = None, sample_weight = None) [source] # Compute Cohen’s kappa: a statistic that measures inter-annotator agreement. My code is as follows. normalize: If False, plot the raw numbers. Here the [Y, N] are the defined class labels and can be extended. Major Feature metrics. Jul 13, 2013 · import numpy as np import perfplot import scipy from sklearn. This normalisation will ensure that random guessing will yield a score of 0 in expectation, and it is upper bounded by Normalizes confusion matrix over the true (rows), predicted (columns) conditions or all the population. arrays true and pred. Recursively merges pair of clusters of sample data; uses linkage distance. Parameters: n_clustersint or None, default=2. Where G is the Gini coefficient and AUC is the ROC-AUC score. So these cell values of the confusion matrix are addressed the above questions we have. ConfusionMatrixDisplay(confusion_matrix, *, display_labels=None) [source] ¶. By definition a confusion matrix \(C\) is such that \(C_{i, j}\) is equal to the number of observations known to be in group \(i\) and predicted to be in Jun 3, 2018 · The confusion matrix is computed by metrics. Usage. plot_confusion_matrix(cm = cm, # confusion matrix created by. Fitted classifier or a fitted Pipeline in which the last estimator is a classifier. metrics: confusion_matrix; accuracy_score sklearn. An optional second feature array. target_names = y_labels_vals, # list of names of the classes. metrics import confusion_matrix print confusion_matrix(y_test, preds) And once you have the confusion matrix, you can plot it. If metric is “precomputed”, X is assumed to be a distance matrix and must be square during fit. adjusted_rand_score (labels_true, labels_pred) [source] # Rand index adjusted for chance. The best value is 1 and the worst value In multilabel confusion matrix M C M, the count of true negatives is M C M:, 0, 0, false negatives is M C M:, 1, 0 , true positives is M C M:, 1, 1 and false positives is M C M:, 0, 1. ensemble import AdaBoostClassifier, GradientBoostingClassifier from sklearn. display_labelsarray-like of shape (n_classes,), default=None. It supports various distance metrics, such as Euclidean distance, Manhattan distance, and more. ''' Aug 3, 2020 · FN: (8 - 6), the remaining 2 cases will fall into the true negative cases. 0 and will be removed in 1. 0 / n_features. Compute Pearson’s r for each features and the target. The pairwise method can be used to compute pairwise distances between samples in the input arrays. The problem is that it has the same shape as it had before, but when you evaluate accuracy you need a vector of labels. Best possible score is 1. Example of confusion matrix usage to evaluate the quality of the output of a classifier on the iris data set. It must be None if distance_threshold is not None. The pair confusion matrix \(C\) computes a 2 by 2 similarity matrix between two clusterings by considering all pairs of samples and counting pairs that are assigned into the same or into different clusters under the true and predicted test_sizefloat or int, default=None. metrics import plot_confusion_matrix y_tr Aug 5, 2018 · In this tutorial, we will walk through a few of these metrics and write our own functions from scratch to understand the math behind a few of them. X {array-like, sparse matrix} of shape (n_samples_X, n_features) A feature array. metrics import confusion_matrix from keras. pyplot as plt from keras. applications import MobileNet from sklearn. 2. datasets. y_pred 1d array-like, or label indicator array / sparse matrix. The metric to use when calculating distance between instances in a feature array. 1. ¶. By definition a confusion matrix C is such that C i, j is equal to the number of observations known to be in group i and predicted to be in group j. pairwise. Let’s pick a dataset, train a model and evaluate its performance using a confusion matrix. If 'filename', the sequence passed as an argument to fit is expected to be a list of filenames that need reading to fetch the raw content to analyze. Compute the balanced accuracy. 2,1. confusion_matrix(y_true, y_pred, labels=None) [source] ¶ Compute confusion matrix to evaluate the accuracy of a classification. feature_selection. In the latter case, the scorer object will sign-flip the outcome of the score_func. data y = dataset. accuracy_score(y_true, y_pred, *, normalize=True, sample_weight=None) [source] #. This is an example showing how scikit-learn can be used to classify documents by topics using a Bag of Words approach. seed(42) X, y = make_classification(1000, 10, n_classes=2) clf = RandomForestClassifier() clf. Some of them are discussed below: Confusion Matrix: A confusion matrix is a table that summarizes the performance of a classification algorithm. metrics import confusion_matrix import matplotlib. The recall is the ratio tp / (tp + fn) where tp is the number of true positives and fn the number of false negatives. r_regression(X, y, *, center=True, force_finite=True) [source] #. Use one of the following class methods: from_predictions or from_estimator. ndarray' object is not callable 0 sklearn. y_true = [0, 1, 0, 1] y_pred = [0, 1, 1, 0] cm = confusion_matrix(y_true, y_pred) Once you have created the confusion matrix, you can plot it using the `plot_confusion_matrix` function. validation. A feature array. F1 scores are the harmonic means of precision and recall. metrics import confusion_matrix. Blues. Whether score_func is a score function (default), meaning high is good, or a loss function, meaning low is good. mutual_info_score(labels_true, labels_pred, *, contingency=None) [source] #. All parameters are stored as attributes. euclidean_distances (X, Y = None, *, Y_norm_squared = None, squared = False, X_norm_squared = None) [source] # Compute the distance matrix between each pair from a vector array X and Y. spatial. Furthermore, the output can be arbitrarily high when y_true is small (which is specific to the metric) or when abs(y_true-y_pred) is large (which is common for most regression metrics). from sklearn. matthews_corrcoef# sklearn. Updating for multilabel classification visualization. If None, defaults to 1. svm import SVC from sklearn. Dec 26, 2023 · A: The `plot_confusion_matrix` function requires the dataset to be in a format that is supported by the `sklearn. Extending the basic confusion matrix to plot of a grid of subplots with the title as each of the classes. metrics import confusion_matrix" However, this function avoids the dependency on sklearn. tick_params. confusion_matrix(y_true, y_pred, labels=None, sample_weight=None) [source] Compute confusion matrix to evaluate the accuracy of a classification. It provides precision, recall, and F1 score at individual and Classification of text documents using sparse features — scikit-learn 1. Let's try to do it in a reproducible fashion: from sklearn. metrics import plot_confusion_matrix from sklearn. RocCurveDisplay. models import Model import matplotlib. set_ylabel and ax. class sklearn. Default is “minkowski”, which results in the standard Euclidean distance when p = 2. normalize = True, # show proportions. Dec 4, 2019 · ImportError: cannot import name 'plot_confusion_matrix' from 'sklearn. Use sns. Whether to return dense output even when the input is sparse. To prevent such non-finite numbers to pollute higher-level experiments such as a sklearn. . Contingency Matrix# Contingency matrix (sklearn. (Set binary to True, use_idf to False and norm to None to get 0/1 outputs). It is defined as the average of recall obtained on each class. calinski_harabaz_score(X sklearn. Parameters: estimatorestimator instance. Proof. pairwise_distances (X, Y = None, metric = 'euclidean', *, n_jobs = None, force_all_finite = True, ** kwds) [source] # Compute the distance matrix from a vector array X and optional Y. This does not mean outputs will have only 0/1 values, only that the tf term in tf-idf is binary. metricstr or callable, default=”euclidean”. metrics import confusion_matrix predictions_one_hot = model. 25. get_cmap('jet') or plt. Estimated targets as returned by a classifier. haversine_distances (X, Y = None) [source] # Compute the Haversine distance between samples in X and Y. If the input is a vector array, the distances are computed. AP summarizes a precision-recall curve as the weighted mean of precisions achieved at each threshold, with the increase in recall from the previous threshold used as the Build a text report showing the main classification metrics. It returns a distance matrix representing the Apr 17, 2023 · How to create them using Sklearn’s powerful functions; How to create common confusion matrix metrics, such as accuracy and recall, using sklearn; How to visualize a confusion matrix using Sklearn and Seaborn The Gini Coefficient is a summary measure of the ranking ability of binary classifiers. average_precision_score(y_true, y_score, *, average='macro', pos_label=1, sample_weight=None) [source] #. The recall is intuitively the ability of the greater_is_better bool, default=True. argmax(axis=1)) print(cm) Output would be something like this: sklearn. set to change the font size of the heatmap values. Target names used for plotting. FP: We are having 2 negative cases and 1 we predicted as positive. datasets import make_classification from sklearn. set_title, and the font size of the tick labels with ax. This article adjusted_rand_score# sklearn. load_iris (*, return_X_y = False, as_frame = False) [source] # Load and return the iris dataset (classification). By definition a confusion matrix \(C\) is such that \(C_{i, j}\) is equal to the number of observations known to be in group \(i\) but predicted plt. metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report, confusion_matrix # We use a utility to generate artificial classification data. matthews_corrcoef (y_true, y_pred, *, sample_weight = None) [source] # Compute the Matthews correlation coefficient (MCC). It is recommend to use from_estimator or from_predictions to create a ConfusionMatrixDisplay. The Silhouette Coefficient is calculated using the mean intra-cluster distance (a) and the mean nearest-cluster distance (b) for each sample. argmax(y_pred, axis=1) instead to output correct labels. Where | U i | is the number of the samples in cluster U i and | V j | is the number of the samples in cluster V j, the . confusion_matrix` function. If you would prefer to just read about performance metrics, please see my previous post at here. Returned confusion matrices will be in the order of sorted unique labels in If True, all non-zero term counts are set to 1. metrics. confusion_matrix`. pyplot as plt PLOTS = '/plots/' # Output folder def plt_confusion_matrix(y_test, y_pred, normalize=False, title="Confusion matrix"): """ Plots a nice confusion matrix. Multiclass data will be treated as if binarized under a one-vs-rest transformation. Returns: kernel ndarray of shape (n_samples_X, n_samples_Y) The Aug 9, 2019 · Link to my confusion matrix image. This method takes either a vector array or a distance matrix, and returns a distance matrix. confusion_matrix(y_true, y_prediction), but that just shifts the problem. load_iris() X = dataset. confusion_matrix — scikit-learn 0. 17. It is expressed using the area under of the ROC as follows: G = 2 * AUC - 1. contingency_matrix) reports the intersection cardinality for every true/predicted cluster pair. random. flatten(), vis_arr, labels): plot_confusion_matrix is deprecated in 1. Y{array-like, sparse matrix} of shape (n_samples_Y, n_features), default=None. The balanced accuracy in binary and multiclass classification problems to deal with imbalanced datasets. You can specify the font size of the labels and the title as a dictionary in ax. jaccard_similarity_score (y_true, y_pred, normalize=True, sample_weight=None) [source] ¶ Jaccard similarity coefficient score The Jaccard index [1], or Jaccard similarity coefficient, defined as the size of the intersection divided by the size of the union of two label sets, is used to compare set of predicted labels for a sklearn. By default, labels will be used if it is defined, otherwise the unique labels of y_true and y_pred import numpy as np from sklearn import metrics from scipy. Oct 18, 2023 · To check the accuracy of classifications, we use the different-different metrics. ax (matplotlib. pairwise submodule implements utilities to evaluate pairwise distances or affinity of sets of samples. #14357 by Thomas Fan. argmax(axis=1), predictions_one_hot. If the input is a vector array, the distances are Dec 9, 2020 · The answer above is the right one. The function itself relies on other functions - one defined in the same module and others is from sklearn. 3 documentation; 第一引数に実際のクラス（正解クラス）、第二引数に予測したクラスのリストや配列を指定する。 sklearn. predict(test_data) cm = confusion_matrix(labels_one_hot. This function introduces the visualization API described in the User Guide. See the documentation of scipy. -----. from_estimator : Plot the confusion matrix given an estimator, the data, and the label. keyboard_arrow_up. For those who cannot upgrade/install from source, below is the required code. 8. epsfloat, default=None. metrics import plot_confusion_matrix sklearn. Y{array-like, sparse matrix} of shape (n Parameters: input{‘filename’, ‘file’, ‘content’}, default=’content’. Gallery examples: Lagged features for time series forecasting Poisson regression and non-normal loss Quantile regression Tweedie regression on insurance claims sklearn. EDIT after @seralouk's answer. Results are identical (and similar in computation time) to: "from sklearn. RocCurveDisplay(*, fpr, tpr, roc_auc=None, estimator_name=None, pos_label=None) [source] #. Build a contingency matrix describing the relationship between labels. May 9, 2020 · import numpy as np def compute_confusion_matrix(true, pred): '''Computes a confusion matrix using numpy for two np. 20. 5670588235294117 What is Classification Report? It is a python method under sklearn metrics API, useful when we need class-wise metrics alongside global metrics. confusion_matrix. from_predictions : Plot the confusion matrix given the true and predicted labels. Ground truth (correct) target values. The rows being the samples and the columns being: Sepal Length, Sepal Width, Petal Length and Petal Width. In multilabel classification, this function computes subset accuracy: the set of labels predicted for a sample must exactly match the corresponding set of labels in y_true. distance Apr 18, 2019 · 混同行列を生成: confusion_matrix() scikit-learnで混同行列を生成するにはconfusion_matrix()を用いる。 sklearn. in scikit-learn==1. cluster. Unexpected token < in JSON at position 4. If metric is a string, it must be one of the options allowed by scipy. If False, the output is sparse if both input arrays are sparse. accuracy_score. ROC Curve visualization. This module contains both distance metrics and kernels. Pearson’s r is also known as the Pearson correlation coefficient. fit(X,y) cm = plot See Also -------- confusion_matrix : Compute Confusion Matrix to evaluate the accuracy of a classification. normalized_mutual_info_score (labels_true, labels_pred, *, average_method = 'arithmetic') [source] # Normalized Mutual Information between two clusterings. The diagonal elements represent the number of points for which the predicted label is equal to the true label, while off-diagonal elements are those that are mislabeled by the classifier. Here is the function I use: from sklearn. Linear model for testing the individual effect of each of many regressors. Compute the distance matrix from a vector array X and optional Y. If True, plot the proportions. Mutual Information between two clusterings. By definition a confusion matrix \(C\) is such that \(C_{i, j}\) is equal to the number of observations known to be in group \(i\) but predicted to be sklearn. utils. cross_validation import StratifiedShuffleSplit from sklearn. TN: Out of 2 negative cases, the model predicted 1 negative case correctly. This tutorial will cover the following metrics from sklearn. model_selection import train_test_split from sklearn. ConfusionMatrixDisplay. Apr 3, 2020 · Let's use the good'ol iris dataset to reproduce this, and fit several classifiers to plot their respective confusion matrices with plot_confusion_matrix:. Read more in the User Guide . The sklearn. The iris dataset is a classic and very easy multi-class classification dataset. plot(). Returned confusion matrices will be in the order of sorted unique labels in Something went wrong and this page crashed! If the issue persists, it's likely a problem on our side. It can be different result in float64 and float16. copy (boolean, optional) – Determines whether fit is used on clf or on a copy of clf. This metric computes the number of times where the correct label is among the top k labels predicted (ranked by predicted scores). 0 and represent the proportion of the dataset to include in the test split. confusion_matrix - TypeError: 'numpy. Mar 19, 2020 · then print the confusion matrix using the confusion_matrix function from sklearn. cm. If None, the value is set to the complement of the train size. Top-k Accuracy classification score. silhouette_score (X, labels, *, metric = 'euclidean', sample_size = None, random_state = None, ** kwds) [source] # Compute the mean Silhouette Coefficient of all samples. Agglomerative Clustering. confusion_matrix (y_true, y_pred, labels=None, sample_weight=None) [source] ¶ Compute confusion matrix to evaluate the accuracy of a classification. If int, represents the absolute number of test samples. Classification of text documents using sparse features. Note that the multilabel case isn’t covered here. sklearn. Y {array-like, sparse matrix} of shape (n_samples_Y, n_features), default=None. Jun 10, 2015 · Confusion Matrix tells us about the distribution of our predicted values across all the actual outcomes. labels_ metrics. By definition a confusion matrix is such that is equal to the number of observations known to be in group but predicted to be in group . The laplacian kernel is defined as: K(x, y) = exp(-gamma ||x-y||_1) for each pair of rows x in X and y in Y. Parameters: labels_truearray-like of shape (n_samples,) Ground truth class labels to be used as a reference. If the input is a distances matrix, it is returned instead. optimize import linear_sum_assignment def cluster_accuracy(y_true, y_pred): # compute contingency matrix (also called confusion matrix) contingency_matrix = metrics. The number of clusters to find. Although the terms might sound complex, their underlying concepts are pretty straightforward. It is defined as Explained variance regression score function. For float64, For float16, sklearn. confusion_matrix(y_true, y_pred, *, labels=None, sample_weight=None, normalize=None) [source] ¶. If X is the distance array itself, use “precomputed” as the metric. They are based on simple formulae and can be easily calculated. Jul 26, 2016 · from sklearn import metrics from sklearn. # sklearn. 3. confusion_matrix (y_true, y_pred, *, labels = None, sample_weight = None, normalize = None) [source] # Compute confusion matrix to evaluate the accuracy of a classification. Read more in the User Guide. cannot import name 'plot_confusion_matrix' from 'sklearn. The Rand Index computes a similarity measure between two clusterings by considering all pairs of samples and counting pairs that are assigned in the same or different clusters in the predicted and true clusterings. Confusion Matrix visualization. plot_roc_curve has been added to plot roc curves. set_xlabel, ax. target import numpy as np from sklearn. So, it is a Confusion matrix. subplots(4, 4, figsize=(12, 7)) for axes, cfs_matrix, label in zip(ax. distance and the metrics listed in distance_metrics for valid metric values. Edit : As you have no test data seperately, you will test on X_iris. dense_outputbool, default=True. confusion_matrix(y_true, y_pred, *, labels=None, sample_weight=None, normalize=None) [source] ¶. A brief summary is given on the two here. Apr 7, 2022 · How can I save a confusion matrix as png? I've saw this answer: How to save Confusion Matrix plot so that I can call it for future reference? from sklearn. We will consider the heart-disease dataset from Kaggle for building a model to predict whether the patient is prone to heart disease or not. My code is the following: Metric to use for distance computation. Here, the class -1 is to be considered as the negatives, while 0 and 1 are variations of positives. content_copy. 5. ndarray. If train_size is also None, it will be set to 0. If 'file', the sequence items must have a ‘read’ method (file-like object) that is called to fetch the I am trying to use ax_ and matplotlib. The Mutual Information is a measure of the similarity between two labels of the same data. By default, labels will be used if it is defined, otherwise the unique labels of y_true and y_pred will be used. com Dataset transformations. In multilabel confusion matrix M C M, the count of true negatives is M C M:, 0, 0, false negatives is M C M:, 1, 0 , true positives is M C M:, 1, 1 and false positives is M C M:, 0, 1. gamma float, default=None. cm = confusion_matrix(y_test, rf_predictions) May 9, 2020 · For your problem to work as you expect it you should do cm. cluster import KMeans kmeans_model = KMeans(n_clusters=3, random_state=1). Aug 6, 2019 · I am running a feed forward neural network and want to get a confusion matrix with the line sklearn. metrics import categorical Jan 6, 2023 · from sklearn. confusion_matrix sklearn. The first coordinate of each point is assumed to be the latitude, the second is the longitude, given in sklearn. pair_confusion_matrix# sklearn. Type of the matrix returned by fit_transform () or transform (). confusion_matrix(goldLabel_array, predictions, sample_weight=None, labels=None) But whe Returning None is useful for in-place operations, rather than reductions. ndarray' object is not callable Jul 15, 2015 · from sklearn. The below plot uses the first two features. linear_model import LogisticRegression from matplotlib import pyplot as You can do this using the `confusion_matrix` function. If a float, that value is added to all values in the contingency matrix. Feb 16, 2022 · sklearn. axes. If None, confusion matrix will not be normalized. dtypedtype, default=float64. It consists of four metrics: True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN). fig, ax = plt. The contingency matrix provides sufficient statistics for all clustering metrics where the samples are independent and identically distributed and one doesn’t need to account for some from sklearn. labels_predarray-like of shape (n_samples,) Cluster labels to evaluate. pairwise import cosine_similarity` is the best. 0, lower values are worse. Accuracy classification score. #. optimizers import Adam from keras. If None, pairwise_distances_chunked returns a generator of vertical chunks of the distance matrix. Share Jan 10, 2021 · import os import keras import numpy as np import tensorflow as tf from keras. SyntaxError: Unexpected token < in JSON at position 4. To do this take np. colors. Compute average precision (AP) from prediction scores. Accuracy_scores, Recall(sensitivity), Precision, Specificity and other similar metrics are subsets of Confusion Matrix. 0 and 1. The Matthews correlation coefficient is used in machine learning as a measure of the quality of binary and multiclass classifications. Pairwise metrics, Affinities and Kernels #. Jun 11, 2018 · You can also add these two more metrics: from sklearn. Refresh. For efficiency reasons, the euclidean distance between a pair of row vector x and y is computed as: Gallery examples: Early stopping in Gradient Boosting Gradient Boosting regression Prediction Intervals for Gradient Boosting Regression Model Complexity Influence Linear Regression Example Poisson Jan 3, 2021 · This article also includes ways to display your confusion matrix AbstractAPI-Test_Link Introduction Accuracy, Recall, Precision, and F1 Scores are metrics that are used to evaluate the performance of a model. r_regression. bw me lh cd dx de fq gs gm cb