Calculating the Best Performance Achievable in a Classification Problem¶
In this tutorial we show how to run to use the kxy
package to estimate the best performance that can be achieved when using a specific set of variables in a regression problem.
We use the UCI bank note classification dataset.
In [1]:
%load_ext autoreload
%autoreload 2
import warnings
warnings.filterwarnings('ignore')
# Required imports
import pandas as pd
import kxy
In [2]:
url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/00267/' \
'data_banknote_authentication.txt'
df = pd.read_csv(url, names=['Variance', 'Skewness', 'Kurtosis', 'Entropy', 'Is Fake'])
How To Generate The Achievable Performance Analysis¶
In [3]:
performance_analysis = df.kxy.achievable_performance_analysis('Is Fake', \
input_columns=()) # Use all columns but the label column as inputs
In [4]:
performance_analysis
Out[4]:
Achievable R^2 | Achievable Log-Likelihood Per Sample | Achievable Accuracy |
---|---|---|
0.684734 | -0.115977 | 0.975 |
Note: the same syntax is used for regression problems. The type of supervised learning problem is inferred based on the label column.
Column Meaning¶
Achievable R^2
: The highest \(R^2\) that can be achieved by a model using the inputs to predict the label.Achievable True Log-Likelihood Per Sample
: The highest true log-likelihood per sample that can be achieved by a model using the inputs to predict the label.Achievable Accuracy
: The highest classification accuracy that can be achieved by a model using the inputs to predict the label.
These performances are not mere upper bounds, they are attainable.