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Uncertainty in Multiclass Classification

Dr. Rishabh Pathak
1. What is Uncertainty in Classification? Uncertainty refers to the model’s confidence or doubt in its predictions. Quantifying uncertainty is important to understand how reliable each prediction is. In multiclass classification , uncertainty estimates provide probabilities over multiple classes, reflecting how sure the model is about each possible class. 2. Methods to Estimate Uncertainty in Multiclass Classification Most multiclass classifiers provide methods such as: predict_proba: Returns a probability distribution across all classes. decision_function: Returns scores or margins for each class (sometimes called raw or uncalibrated confidence scores). The probability distribution from predict_proba captures the uncertainty by assigning a probability to each class. 3. Shape and Interpretation of predict_proba in Multiclass Output shape: (n_samples, n_classes) Each row corresponds to the probabilities of ...
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Matplotlib

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Dr. Rishabh Pathak

matplotlib is the primary scientific plotting library in Python, widely used for creating static, interactive, and animated visualizations in data analysis and scientific computing.

Core Features of matplotlib:

  1. Wide Range of Plot Types: matplotlib enables the creation of various common and complex plots such as:
  • Line charts
  • Histograms
  • Scatter plots
  • Bar charts
  • Pie charts
  • Error bars
  • 3D plotting (via mpl_toolkits.mplot3d)

This versatility makes it a fundamental visualization tool for exploratory data analysis and presentation-quality graphics.

2.      High Quality and Customizability: The library allows fine-grained control over all aspects of a plot including lines, markers, colors, labels, legends, axes, ticks, grid lines, figure size, and fonts. Thus, it supports the creation of publication-quality figures.

3.      Integration with Jupyter Notebook: matplotlib integrates well with interactive programming environments such as Jupyter Notebook, allowing inline and interactive plotting:

  • %matplotlib inline renders static plots embedded within notebook cells.
  • %matplotlib notebook provides interactive figures with zooming and panning capabilities.

This makes visualization an integral part of the iterative data exploration process.

  1. Support for Multiple Output Formats: matplotlib can save figures to a variety of file formats such as PNG, PDF, SVG, EPS, and more, suitable for reports and publications.

Usage Example:

The following code snippet creates a simple plot of a sine function, demonstrating basic usage:

%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
 
# Generate 100 numbers between -10 and 10
x = np.linspace(-10, 10, 100)
# Compute sine of x
y = np.sin(x)
# Plot y vs x with 'x' markers
plt.plot(x, y, marker="x")
plt.show()

Practical Applications:

·         Exploratory Data Analysis (EDA): Quickly visualize data distributions, trends, and relationships.

·         Model Diagnostics: Plot residuals, learning curves, confusion matrices, and other metrics in machine learning.

·         Presentation and Reporting: Generate clear visual representations to communicate insights and findings.

Summary

matplotlib is a comprehensive and versatile plotting library that forms the backbone of data visualization in Python. Its integration with NumPy arrays, interactive support in environments such as Jupyter Notebook, and extensive customization options make it an essential tool for both exploratory data analysis and producing publication-quality graphics.

 

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