Skip to main content
4 votes
Accepted

How to interpret the results of a classifier when train/test method gives much better results than cross validated one?

What does these varying scores represent, particularly the low scores of cross validation? Together, they represent the fact that error estimtes based on a small number of tested cases are highly ...
cbeleites unhappy with SX's user avatar
4 votes
Accepted

What is happening behind the scenes when we use CalibratedClassifierCV without prefit?

When we use cv=prefit, we will split the data into train, test and calibration sets, then fit a model using train sets, calibrate with calibration set and later use the calibrated model with the test ...
Ben Reiniger's user avatar
  • 4,663
1 vote

Finding the corners of noisy polygons

The following (using Mathematica) does not do what one would do "by eye" but that's because the data points don't fall perfectly on a desired number of line segments. This uses Mathematica's ...
JimB's user avatar
  • 3,889
1 vote
Accepted

Finding the corners of noisy polygons

Here's a shot at it using the Doglas-Peuker algorithm as whuber suggested. Using MATLAB ...
sav's user avatar
  • 229
1 vote
Accepted

How does average_precision_score metric in scikit-learn work for non-probability prediction scores

You don't have to set thresholds on a probability scale. If your predictions are y_score = np.array([0.8, 3.4, 8.35, 12.1]), a perfectly reasonable set of ...
Dave's user avatar
  • 63.7k
1 vote

Do you need to adjust the probability if you use the 'class_weight' parameter in LogisticRegression-sklearn?

You seem to be mixing up a few notions that often get mixed up in machine learning work. You start out by saying you want the probabilities and use a logistic regression to estimate them. Outstanding! ...
Dave's user avatar
  • 63.7k

Only top scored, non community-wiki answers of a minimum length are eligible