PUM2023W 02 Regression скачать в хорошем качестве

PUM2023W 02 Regression

   • PUM2023W 02 Regression   Regression, Data Types, and the Machine Learning Pipeline Defining Intelligence in the Context of AI In artificial intelligence, intelligence is the ability to efficiently behave in a new situation. Efficiently: A system needs a goal function (e.g., fastest vs. shortest route) to define "best." Behave: The system must execute decisions. An agent (software) makes the call, and the body (hardware) performs it. New Situation: The system must generalize knowledge to novel environments. This is called transfer. The Core Machine Learning Problems Regression: Predicting a numerical variable. Classification: Predicting a categorical variable. Segmentation (Clustering): Finding hidden patterns without a target variable. Regression and classification are both prediction problems. Understanding Regression If you plot flat sizes against sales prices, a linear pattern often emerges. A trend line expressed as an equation (e.g., y = 79.9 * x) allows a model to estimate the price of a flat size it has never seen before, demonstrating intelligence through generalization. Multiple Regression and Feature Engineering Real-world problems involve many variables (features). In the Ames Housing dataset, using 79 features instead of one can drop the Mean Absolute Percentage Error (MAPE) from 26% to 11%. Machine learning excels at detecting these multi-dimensional patterns. Common Regression Metrics: R2 Score: Measures explained variance. RMSE (Root Mean Square Error): Measures standard deviation of errors. MAPE: Expresses error as a percentage for business clarity. Feature Importance and Business Value Feature Importance identifies which variables impact predictions most. In housing, these are often Overall Quality and Living Area. Business Applications: Automated Valuation: Objective property pricing. Arbitrage: Finding undervalued assets. Strategic Renovation: Identifying which upgrades add the most value. Predicting Customer Lifetime Value (CLV) Regression can predict total future revenue from a customer based on their first purchase, location, and behavior. Correlation vs. Causation Models find correlations (variables moving together), not necessarily causes. Ice Cream and Murders: Both increase due to heat (the confounder). Fire Trucks and Damage: Large fires cause both. To find true causation, businesses use A/B testing or Causal Machine Learning. The Machine Learning Pipeline A pipeline is a sequence of actions on artifacts (data/models). Training Stage: Historical data is used to create a trained model. Inference Stage: The model predicts values for new data. Strategy Tip: Focus marketing on "remaining potential" (Predicted CLV minus Spend to Date) rather than just the highest predicted spenders. Data Types and Visualization Numerical Variables: Quantitative measurements (price, weight). You can calculate metrics like mean or standard deviation. Categorical Variables: Qualitative descriptions (region, gender). You can only count frequencies. Nominal: No order (Dog, Cat). Ordinal: Logical sequence (Small, Medium, Large). Interval: Binned ranges (Age 18-24). Note: IDs and Zip Codes are categorical, even if they are numbers. Visualization Guide: Numerical across Categorical: Bar Chart. Distribution of one Numerical: Histogram. Correlation of two Numericals: Scatter Plot. Numerical over Time: Line Chart. Target Variable Type: Numerical Target = Regression. Categorical Target = Classification.