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Bookmark or Share- matplotlib: disregard outliers when plotting - Stack OverflowRemoving the outliers would not have the same effect as just rescaling. Automatically finding appropriate axes limits seems generally more desirable and easier than detecting and removing outliers. Here's an autoscale idea using percentiles and data-dependent margins to achieve a nice view.
- Outlier detection using Gaussian mixture - Stack OverflowIt depends therefore on you having some prior knowledge on the inlier/outlier distributions. You can also consider outliers as points which are in some sense 'isolated', and you can try to detect these using unsupervised clustering such as K means, then classifying outliers as those which belong to clusters with fewer than N points.
- Detect and exclude outliers in a pandas DataFrame# Drop the outliers on every attributes drop_numerical_outliers(train_df) # Plot the result. All outliers were dropped. Note that the red points are not # the same outliers from the first plot, but the new computed outliers based on the new data-frame. scatterAreaVsPrice(train_df)
- Is there a numpy builtin to reject outliers from a listLinear outliers can be found by numpy std function, however, if the data is non-linear, for example, a parabola or cubic function, standard deviation will not handle the task well, since it needs regression to help working out the outliers.
- Target Variable has Outliers : Machine Learning RegressionI am currently working on a regression problem where the target variable has close to 2000 outliers against 54000 non outliers. I would like to know how do we deal with data where the target variable has outliers?? Things i have tried so far: Taking entire train data including outliers - score is ok ok
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