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An ROC curve or Receiver Operating Characteristic curve is used to illustrate the performance of a binary classifier. A point on the ROC curve is created by plotting TPR (true positive rate) -vs- FPR (false positive rate) of a classifier. The ROC curve is created by evaluating the classifier at different discrimination thresholds. TPR = TP/(TP FN) = Recall or Sensitivity FPR = FP/(FP TN) = Fall-out or (1 - Specificity). A random guess should be expected to fall on the diagonal line from (0,0) to (1,1).

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rootmain | rootmain.com Reviews

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An ROC curve or Receiver Operating Characteristic curve is used to illustrate the performance of a binary classifier. A point on the ROC curve is created by plotting TPR (true positive rate) -vs- FPR (false positive rate) of a classifier. The ROC curve is created by evaluating the classifier at different discrimination thresholds. TPR = TP/(TP FN) = Recall or Sensitivity FPR = FP/(FP TN) = Fall-out or (1 - Specificity). A random guess should be expected to fall on the diagonal line from (0,0) to (1,1).

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1 rootmain

2 roc curve

3 references

4 output

5 v=ehserlpjwuu

6 weights learned

7 v=eqwr3ggcdzw

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9 numpy masked arrays

10 older posts

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rootmain | rootmain.com Reviews

https://rootmain.com

An ROC curve or Receiver Operating Characteristic curve is used to illustrate the performance of a binary classifier. A point on the ROC curve is created by plotting TPR (true positive rate) -vs- FPR (false positive rate) of a classifier. The ROC curve is created by evaluating the classifier at different discrimination thresholds. TPR = TP/(TP FN) = Recall or Sensitivity FPR = FP/(FP TN) = Fall-out or (1 - Specificity). A random guess should be expected to fall on the diagonal line from (0,0) to (1,1).

INTERNAL PAGES

rootmain.com

1

rootmain: Overfitting a noisy dataset with a higher order hypothesis

http://www.rootmain.com/2013/05/overfitting-noisy-dataset-with-higher.html

Overfitting a noisy dataset with a higher order hypothesis. The following example illustrates a scenario where the usage of a complex hypothesis causes overfitting. The initial target function ( blue dotted line. Is a 2nd order polynomial with some noise added to it (red points in the plot below). For the hypothesis, 2 approaches are tested. With a 15th order polynomial ( green line. The 2nd order polynomial ( magenta line. Prof Yaser's lecture #11 on overfitting : http:/ www.youtube.com/watch?

2

rootmain: Using KFold cross validation in scikit learn

http://www.rootmain.com/2013/05/using-kfold-cross-validation-in-scikit.html

Using KFold cross validation in scikit learn. The idea in KFold cross validation is to split the dataset into K subsets, and use K-1 of these for training and the remaining one for testing. In each iteration we cycle through the different subsets, thereby giving each subset a chance to be the test set. The following is an illustration of KFold cross validation using scikit-learn. Following is a use case with the Boston housing prices data set.

3

rootmain: ROC curve

http://www.rootmain.com/2015/07/roc-curve.html

An ROC curve or Receiver Operating Characteristic curve is used to illustrate the performance of a binary classifier. A point on the ROC curve is created by plotting TPR (true positive rate) -vs- FPR (false positive rate) of a classifier. The ROC curve is created by evaluating the classifier at different discrimination thresholds. TPR = TP/(TP FN) = Recall or Sensitivity FPR = FP/(FP TN) = Fall-out or (1 - Specificity). A random guess should be expected to fall on the diagonal line from (0,0) to (1,1).

4

rootmain: Installing PyBrain on Mountain Lion

http://www.rootmain.com/2013/05/installing-pybrain-on-mountain-lion.html

Installing PyBrain on Mountain Lion. Step 1: Clone the git repository for pybrain. Git clone git:/ github.com/pybrain/pybrain.git Cloning into 'pybrain'. remote: Counting objects: 11252, done. remote: Compressing objects: 100% (3074/3074), done. remote: Total 11252 (delta 8026), reused 11087 (delta 7906) Receiving objects: 100% (11252/11252), 12.85 MiB 1.12 MiB/s, done. Resolving deltas: 100% (8026/8026), done. Step 2: Run setup.py. In [1]: from pybrain.tools.shortcuts import buildNetwork.

5

rootmain: Regularization and the effect of alpha

http://www.rootmain.com/2013/05/regularization-and-effect-of-alpha.html

Regularization and the effect of alpha. When a higher order hypothesis is fit against noisy data, the hypothesis tries to fit the noise using the higher degrees of freedom it has. The objective of regularization is to reduce overfitting by putting brakes on the weights the model learns. In the below code, the training data is generated from a 2nd order target function (x-6)* 2 1. With some added noise. By printing out the weights ( reg.coef. Effect of varying alpha.

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An ROC curve or Receiver Operating Characteristic curve is used to illustrate the performance of a binary classifier. A point on the ROC curve is created by plotting TPR (true positive rate) -vs- FPR (false positive rate) of a classifier. The ROC curve is created by evaluating the classifier at different discrimination thresholds. TPR = TP/(TP FN) = Recall or Sensitivity FPR = FP/(FP TN) = Fall-out or (1 - Specificity). A random guess should be expected to fall on the diagonal line from (0,0) to (1,1).

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