class sklearn.linear_model.RANSACRegressor(base_estimator=None, *, min_samples=None, residual_threshold=None, is_data_valid=None, is_model_valid=None, max_trials=100, max_skips=inf, stop_n_inliers=inf, stop_score=inf, stop_probability=0.99, loss='absolute_loss', random_state=None) [source]
 
RANSAC (RANdom SAmple Consensus) algorithm. RANSAC is an iterative algorithm for the robust estimation of parameters from a subset of inliers from the complete data set. Read more in the User Guide.  Parameters 
 
base_estimatorobject, default=None 

Base estimator object which implements the following methods:  
fit(X, y): Fit model to given training data and target values. 
score(X, y): Returns the mean accuracy on the given test data, which is used for the stop criterion defined by stop_score. Additionally, the score is used to decide which of two equally large consensus sets is chosen as the better one. 
predict(X): Returns predicted values using the linear model, which is used to compute residual error using loss function.  If base_estimator is None, then LinearRegression is used for target values of dtype float. Note that the current implementation only supports regression estimators.  
min_samplesint (>= 1) or float ([0, 1]), default=None 

Minimum number of samples chosen randomly from original data. Treated as an absolute number of samples for min_samples >= 1, treated as a relative number ceil(min_samples * X.shape[0]) for min_samples < 1. This is typically chosen as the minimal number of samples necessary to estimate the given base_estimator. By default a sklearn.linear_model.LinearRegression() estimator is assumed and min_samples is chosen as X.shape[1] + 1.  
residual_thresholdfloat, default=None 

Maximum residual for a data sample to be classified as an inlier. By default the threshold is chosen as the MAD (median absolute deviation) of the target values y.  
is_data_validcallable, default=None 

This function is called with the randomly selected data before the model is fitted to it: is_data_valid(X, y). If its return value is False the current randomly chosen sub-sample is skipped.  
is_model_validcallable, default=None 

This function is called with the estimated model and the randomly selected data: is_model_valid(model, X, y). If its return value is False the current randomly chosen sub-sample is skipped. Rejecting samples with this function is computationally costlier than with is_data_valid. is_model_valid should therefore only be used if the estimated model is needed for making the rejection decision.  
max_trialsint, default=100 

Maximum number of iterations for random sample selection.  
max_skipsint, default=np.inf 

Maximum number of iterations that can be skipped due to finding zero inliers or invalid data defined by is_data_valid or invalid models defined by is_model_valid.  New in version 0.19.   
stop_n_inliersint, default=np.inf 

Stop iteration if at least this number of inliers are found.  
stop_scorefloat, default=np.inf 

Stop iteration if score is greater equal than this threshold.  
stop_probabilityfloat in range [0, 1], default=0.99 

RANSAC iteration stops if at least one outlier-free set of the training data is sampled in RANSAC. This requires to generate at least N samples (iterations): N >= log(1 - probability) / log(1 - e**m)
 where the probability (confidence) is typically set to high value such as 0.99 (the default) and e is the current fraction of inliers w.r.t. the total number of samples.  
lossstring, callable, default=’absolute_loss’ 

String inputs, “absolute_loss” and “squared_loss” are supported which find the absolute loss and squared loss per sample respectively. If loss is a callable, then it should be a function that takes two arrays as inputs, the true and predicted value and returns a 1-D array with the i-th value of the array corresponding to the loss on X[i]. If the loss on a sample is greater than the residual_threshold, then this sample is classified as an outlier.  New in version 0.18.   
random_stateint, RandomState instance, default=None 

The generator used to initialize the centers. Pass an int for reproducible output across multiple function calls. See Glossary.    Attributes 
 
estimator_object 

Best fitted model (copy of the base_estimator object).  
n_trials_int 

Number of random selection trials until one of the stop criteria is met. It is always <= max_trials.  
inlier_mask_bool array of shape [n_samples] 

Boolean mask of inliers classified as True.  
n_skips_no_inliers_int 

Number of iterations skipped due to finding zero inliers.  New in version 0.19.   
n_skips_invalid_data_int 

Number of iterations skipped due to invalid data defined by is_data_valid.  New in version 0.19.   
n_skips_invalid_model_int 

Number of iterations skipped due to an invalid model defined by is_model_valid.  New in version 0.19.      References  
1  
https://en.wikipedia.org/wiki/RANSAC  
2  
https://www.sri.com/sites/default/files/publications/ransac-publication.pdf  
3  
http://www.bmva.org/bmvc/2009/Papers/Paper355/Paper355.pdf   Examples >>> from sklearn.linear_model import RANSACRegressor
>>> from sklearn.datasets import make_regression
>>> X, y = make_regression(
...     n_samples=200, n_features=2, noise=4.0, random_state=0)
>>> reg = RANSACRegressor(random_state=0).fit(X, y)
>>> reg.score(X, y)
0.9885...
>>> reg.predict(X[:1,])
array([-31.9417...])
 Methods  
fit(X, y[, sample_weight]) Fit estimator using RANSAC algorithm.  
get_params([deep]) Get parameters for this estimator.  
predict(X) Predict using the estimated model.  
score(X, y) Returns the score of the prediction.  
set_params(**params) Set the parameters of this estimator.    
fit(X, y, sample_weight=None) [source]
 
Fit estimator using RANSAC algorithm.  Parameters 
 
Xarray-like or sparse matrix, shape [n_samples, n_features] 

Training data.  
yarray-like of shape (n_samples,) or (n_samples, n_targets) 

Target values.  
sample_weightarray-like of shape (n_samples,), default=None 

Individual weights for each sample raises error if sample_weight is passed and base_estimator fit method does not support it.  New in version 0.18.     Raises 
 ValueError

If no valid consensus set could be found. This occurs if is_data_valid and is_model_valid return False for all max_trials randomly chosen sub-samples.     
  
get_params(deep=True) [source]
 
Get parameters for this estimator.  Parameters 
 
deepbool, default=True 

If True, will return the parameters for this estimator and contained subobjects that are estimators.    Returns 
 
paramsdict 

Parameter names mapped to their values.     
  
predict(X) [source]
 
Predict using the estimated model. This is a wrapper for estimator_.predict(X).  Parameters 
 
Xnumpy array of shape [n_samples, n_features] 
  Returns 
 
yarray, shape = [n_samples] or [n_samples, n_targets] 

Returns predicted values.     
  
score(X, y) [source]
 
Returns the score of the prediction. This is a wrapper for estimator_.score(X, y).  Parameters 
 
Xnumpy array or sparse matrix of shape [n_samples, n_features] 

Training data.  
yarray, shape = [n_samples] or [n_samples, n_targets] 

Target values.    Returns 
 
zfloat 

Score of the prediction.     
  
set_params(**params) [source]
 
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.  Parameters 
 
**paramsdict 

Estimator parameters.    Returns 
 
selfestimator instance 

Estimator instance.