Introduction to Machine Learning Decision Trees Prof. Kutty Announcements Copyright By PowCoder代写 加微信 powcoder • Midterm Conflict forms are out; see announcement for deadline • Project 1 is due on Wednesday • Lecture 8 available in 3 parts asynchronous recording Today’s Agenda • Recap: Linear Regression with Squared Loss • Section 1: Feature Selection • […]
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1. Where we are doing supervised learning, we have mostly assumed a deterministic function. Imagine instead a world where we are trying to capture a non-deterministic function. In this case, we might see training pairs where the x value appears several times, but with different y values. For example, we might use attributes of humans
Where are we now? XML Template (2011) [10.8.2011–6:17pm] [1–18] K:/IVI/IVI 415994.3d (IVI) [PREPRINTER stage] Kandel et al. 3 Figure 1. The iterative process of wrangling and analysis. One or more initial data sets may be used and new versions may come later. The wrangling and analysis phases overlap. While wrangling tools tend to be separated
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Where are we now? XML Template (2011) [10.8.2011–6:17pm] [1–18] K:/IVI/IVI 415994.3d (IVI) [PREPRINTER stage] Kandel et al. 3 Figure 1. The iterative process of wrangling and analysis. One or more initial data sets may be used and new versions may come later. The wrangling and analysis phases overlap. While wrangling tools tend to be separated
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STAT318 — Data Mining Dr University of Canterbury, Christchurch, Some of the figures in this presentation are taken from “An Introduction to Statistical Learning, with applications in R” (Springer, 2013) with permission from the authors: G. James, D. Witten, T. Hastie and R. Tibshirani. , University of Canterbury 2021 STAT318 — Data Mining ,1 /
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End-of-year Examinations, 2020 STAT318/STAT462-20S2 (C) Family Name First Name Student Number Venue Seat Number _____________________ _____________________ |__|__|__|__|__|__|__|__| ____________________ ________ No electronic/communication devices are permitted. No exam materials may be removed from the exam room. Mathematics and Statistics EXAMINATION End-of-year Examinations, 2020 STAT318-20S2 (C) / STAT462-20S2 (C) Data Mining Examination Duration: 120 minutes Exam Conditions: Closed
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STAT318 — Data Mining Dr University of Canterbury, Christchurch, Some of the figures in this presentation are taken from “An Introduction to Statistical Learning, with applications in R” (Springer, 2013) with permission from the authors: G. James, D. Witten, T. Hastie and R. Tibshirani. , University of Canterbury 2021 STAT318 — Data Mining ,1 /
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Where are we now? XML Template (2011) [10.8.2011–6:17pm] [1–18] K:/IVI/IVI 415994.3d (IVI) [PREPRINTER stage] Kandel et al. 3 Figure 1. The iterative process of wrangling and analysis. One or more initial data sets may be used and new versions may come later. The wrangling and analysis phases overlap. While wrangling tools tend to be separated
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The Australian National University School of Computing, CECS COMP3430/COMP8430 – Data Wrangling – 2021 Lab 5: Classification for Record Linkage Week 8 Overview and Objectives In today’s lab we continue with our record linkage system that we started in labs 3 and 4, this time looking at the classification step as discussed in lectures 17
COMP3430 / COMP8430 Data wrangling Lecture 6: Resolving data quality issues and data cleaning (Lecturer: ) Lecture outline Data quality issues Forms of data pre-processing An overview of data cleaning Impute missing data Smooth noisy data Remove duplicate data Resolve inconsistent data ● ● ● – – – – ● Summary 2 Data quality issues
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