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How to measure the agreement between two raters? - Cohen's Kappa Coefficient

 The kappa coefficient is a statistic that measures the agreement between raters. It is commonly used in mental health and psychosocial studies. The kappa coefficient can be used for scales with more than two categories, and its range of possible values is from −1 to 1.

Cohen’s kappa is a quantitative measure of reliability for two raters that are rating the same thing, correcting for how often the raters may agree by chance.

In other words, a model will have high kappa score if there is a big differefence between the accuracy and null rate error.

kappa = (Observed agreement - Expected Agreement) / ( 1 - Expected agreement )


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Logistic Regression

 Definition:

Logistic Regression is a Supervised learning algorithm that makes use of logistic functions to predict the probability of a Binary outcome.

- outcome limited to two possible outcomes: yes/no, 0/1, or true/false.

Logistic regression analyzes the relationship between one or more independent variables and classifies data into discrete classes. It is extensively used in predictive modeling, where the model estimates the mathematical probability of whether an instance belongs to a specific category or not.

Logistic regression uses a logistic function called a sigmoid function to map predictions and their probabilities. The sigmoid function refers to an S-shaped curve that converts any real value to a range between 0 and 1.

Types of Logistic Regression:

  1. Binary Logistic Regression:
    The dependent variable has only two 2 possible outcomes/classes.
    Example-Male or Female.
  2. Multinomial Logistic Regression:
    The dependent variable has only two 3 or more possible outcomes/classes without ordering.
    Example: Predicting food quality.(Good,Great and Bad).
  3. Ordinal Logistic Regression:
    The dependent variable has only two 3 or more possible outcomes/classes with ordering. Example: Star rating from 1 to 5



Reference:

Wikipedia

Origin of LR


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Linear Discriminant Analysis - LDA Explanation with Numerical Problem

 Linear Discriminant Analysis (LDA) is a dimensionality reduction technique commonly used for supervised classification problems. The goal of LDA is to project the dataset onto a lower-dimensional space while maximizing the class separability.

LDA is very similar to Principal Component Analysis (PCA).

LDA can be performed in 5 steps:

  1. Compute the mean vectors for the different classes from the dataset.
  2. Compute the scatter matrices (in-between-class and within-class scatter matrices).
  3. Compute the eigenvectors and corresponding eigenvalues for the scatter matrices.
  4. Sort the eigenvectors by decreasing eigenvalues and choose k eigenvectors with the largest eigenvalues.
  5. Use this eigenvector matrix to transform the samples onto the new subspace.
 Linear Discriminant Analysis (LDA) is like PCA, but it focuses on maximizing the seperability among the known categories.

Assumptions:
LDA is parametric - assumes Normal Distribution of data.
LDA assumes that each input have same variance.

Why Use Linear Discriminant Analysis?


Dimensionality Reduction
Feature Extraction
Handling Multiclass Problems
Reducing Overfitting

Applications:
image recognition, text classification, bioinformatics, face recognition

Numerical Problem: Conversion of 2D to 1D





References:
LDA clearly Explained - StatQuest
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Data Structures and Algorithms Best Resources to Learn

 



Algorithms & Data Structures

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Fundamentals of Affective Computing

Course Topics



- Fundamentals of Affective Computing
- Emotion Theory and Emotional Design
- Experimental design: Affect Elicitation
- Emotions in facial expressions
- Emotions in Voice
- Emotions in Text
- Emotions in Physiological Signals
- Multimodal Emotion Recognition
- Emotional Empathy in Agents
- Online and Adaptive recognition of Emotions
- Case Study
- Ethical Issues: Ethical, legal and social Implications of Affective Computing


Affective Computing
Definition: The field of affective computing encompasses both the creation of and interaction with machine systems that sense, recognize, respond to and influence emotions.

What is Emotion?

Emotions are physical and mental states brought on by neurophysiological changes, variously associated with thoughts, feelings, behavioral responses, and a degree of pleasure or displeasure. Emotions are often intertwined with mood, temperament, personality, disposition, or creativity.

Affect Sensing:

Affect sensing refers to a system that recognize emotion by receiving data through signals and patterns.

Data:

-Facial activity through camera
low cost and easy to use
privacy issues
color, thermal………

-Gesture, pose activity through camera
-Microphone - vocal
-Textual – Natural Language Processing

Signals:

Physiological signals:

EDA (Electrodermal activity) – conductivity changes in the skin due to increase in the activity of sweat glands.

EEG(Electro-encephalo-graphy) – electrical activity of neurons



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Question 1: Reverse Words in a String III

  def reverseWords(s: str) -> str: words = s.split() return ' '.join(word[::-1] for word in words)