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How would you define highly correlated? Normally one would decide on the threshold, of say Pearson's correlation coefficient. When the magnitude of Pearson's correlation coefficient would be above this value, you would call the two features correlated. The above would help you to look for pairwise correlation. To detect correlation between several features ...


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I've tried to create a function as suggested but it doesn't work for my code. However, as suggested from an example on Kaggle, I found the below solution: import shap #load JS vis in the notebook shap.initjs() #set the tree explainer as the model of the pipeline explainer = shap.TreeExplainer(pipeline['classifier']) #apply the preprocessing to x_test ...


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I want to find out that which models are trained on COCO-Dataset Here you can find state of the art models for instance segmentation on COCO dataset. And here you can find state of the art models for object detection. You will also find open sourced code and models for most of them. But I want to find out that which model has highest accuracy and lowest ...


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If you look at ROC curve you get to see a different picture ROC-AUC curve Uptill point A TPR is increasing faster than FPR so that means precision should be higher here. After point A TPR is increasing at slower rate than FPR and so Precision should start decreasing, after point B the precision should further decrease. Here in ROC from left to right ...


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Hello and welcome to the site! You can use list(range(1,100)) to get what you want. However, questions like this (how to achieve something in Python) are more suitable for stackoverflow. The community here focuses on data science related questions, as the name suggests.


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You can, for example, use "max_depth": np.arange(1, 101, 1) which gives array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, ...


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