Welcome to learning edition of the Data Pragmatist, your dose of all things data science and AI.

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• Cognitive Computing; Practical Applications of AI

• Hugging Face launches open source AI assistant maker to rival OpenAI’s custom GPTs

Today we are talking about comprehending prediction errors, bias, and variance is fundamental for achieving accurate models. As part of our learning series, Top 5 AI resume builders.

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— Arun Chinnachamy

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🧠 Logistic Regression in Machine Learning

Logistic regression is a statistical method used for binary classification, extending from linear regression to predict the probability of a binary outcome.

Logistic regression predicts categorical dependent variables, typically binary (e.g., yes/no, pass/fail), using a sigmoid function to map input variables to probabilities between 0 and 1.

Terminologies and Concepts

• Independent and Dependent Variables: Input features (independent variables) and output (dependent variable) are defined. Logistic function transforms variables to probabilities.

• Logistic Regression Equation: Derivation of the logistic regression equation, converting linear combination of variables to probabilities using the sigmoid function.

• Likelihood Function: Explanation of likelihood function for logistic regression and log-likelihood components.

• Gradient of the Log-Likelihood Function: Derivation of the gradient for maximum likelihood estimation.

Code Implementation

• Binomial Logistic Regression: Python code example for binary classification with the breast cancer dataset, including training, prediction, and evaluation.

• Multinomial Logistic Regression: Python code example for multiple-class classification with the digit dataset, including training, prediction, and evaluation.

Evaluation Metrics

• Accuracy, Precision, Recall, F1 Score: Definition and significance of each metric, along with calculation and interpretation.

• AUC-ROC and AUC-PR: Explanation of ROC and PR curves and the use of area under the curve for model evaluation.

Precision-Recall Tradeoff

Importance of threshold in classification, balancing precision and recall based on application needs, illustrated with examples of different scenarios.