Arbor Decisionis - Introductio

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Questions and Answers

Quod est primum divisio in arboribus decisionis?

Divisio ad sinistram

Simpliciter in duas partes

Arbor representata

Ratio puncta rubra/coerulea (correct)

Quot partes dividuntur in prima divisione arboris decisionis?

Octo partes

Quattuor partes

Duas partes (correct)

Tres partes

Quid efficit arbor decisionis in primo gradu?

Facit decisionem simplex (correct)

Definit lineas directrices

Aestimare variabiles

Generat puncta rubra et coerulea

Quod est effectus divisionis ad sinistram in arboribus decisionis?

Iterat processum divisionis Signup and view all the answers

Quomodo arbor decisionis potest repraesentari?

Ut diagramma Signup and view all the answers

Quae ratio adhibetur ad determinandum regiones in arboribus decisionis?

Ratio puncta rubra et coerulea Signup and view all the answers

Quae est utilitas divisionis secundae in arboribus decisionis?

Ampliat decisionem Signup and view all the answers

Quod est maximum caput informationis cum dividente?

H(p (parent)) - H(p (left)) - H(p (right)) Signup and view all the answers

Quae est utilitas Gini impuritatis in computatione?

Celeritas computationis, non utitur logaritmis Signup and view all the answers

In quibus condiciones informationis lucrum melius est?

In problematicis regressionis Signup and view all the answers

Quot moduli impudentiae adhibentur in difficultate identitatis?

Grafici minime varia, sed per exemplaria diversa Signup and view all the answers

Quid significat 'overfitting' in contextu data mining?

Modellum nimis complexum pro notitia Signup and view all the answers

Quot sunt exemplaria maximi criterii informationis?

Datis continuis et discretis Signup and view all the answers

Quod modum adhibere ubi Gini non sit maxime idoneus?

In regressionibus Signup and view all the answers

Quam utilitas implicatur in quantitativa computatione Gini?

Non adhibet logaritmos Signup and view all the answers

Quod argumentum Gini impuritatis describit probabilitatem?

Probabilitas duorum item a data set seleccionatorum habere diversas classes. Signup and view all the answers

Quoties Gini impuritas aequalis est 0?

Cum data set continet unam solam classem. Signup and view all the answers

Quis inter hoc non est pars functionis perditionis?

Quadratum medio. Signup and view all the answers

Quid significat G(p) in contextu Gini impuritatis?

Aestimatio probabilitatis duorum item diversarum classium. Signup and view all the answers

Quod indicat $p_i$ in formula pro Gini impuritate?

Probabilitas selectionis classis i in data set. Signup and view all the answers

Quis inter haec non est causa defectus 0-1 perditionis pro decisionibus arboribus?

Non potest specificare diversitatem classium. Signup and view all the answers

Quot classes necessariae sunt ad Gini impuritatem maximam?

Omnes classes diversae. Signup and view all the answers

Quid est maximum value Gini impuritatis?

Minor quam 1. Signup and view all the answers

Quoties Gini impuritas minimus est 0, qualis est id data set?

Quando omnes elementa sunt identica. Signup and view all the answers

Quod significat Gini impuritas cum in binario classificatione valet?

Probabilitas selectionis diversorum classes. Signup and view all the answers

Quae sequentes gradus adhibentur ad functionem fθ (x) aestimandam?

Incipe ab vertice et move ad primam divisionem. Signup and view all the answers

Quid significat 'greedy optimisation' in constructione arborum?

Optimum parametra in una solum divisione eligere. Signup and view all the answers

Quid fit si data in training solum unum genus continent?

Generatur folium. Signup and view all the answers

Quod est objective ad selectionem optimarum subdivisionum?

Minimam totam amissionem in partibus eligere. Signup and view all the answers

Quid est L(yi, fθ(xi))?

Functionis amissionis necesse est. Signup and view all the answers

Quod genus amissionis non operatur bene in arboribus decisionum?

Amissio 0-1. Signup and view all the answers

Quid significat 'recurse' in constructura arborum decisionum?

Construens novas arborum structuras pro sinistris et dextris filiis. Signup and view all the answers

Quam adhibere ad decidendum quorum divider optimum?

Per computando totam amissionem in omnibus divisionibus. Signup and view all the answers

Quis est accuratus exitus cum secat = 0.5 et feature = y?

50.8% Signup and view all the answers

Quid indicat accuratum exitum 100% in continuis decision stump?

Feature x, secat = 0.0 Signup and view all the answers

Quod est accuratum exitum cum feature = x et secat = -0.5?

83.2% Signup and view all the answers

Quod principium adhibetur ad spatium dividendum in decision stump continuis?

Wenn xfeature < split Signup and view all the answers

Quid significat accuratum exitum 0%?

Tuus model non operatur. Signup and view all the answers

Quod est accuratum exitum quando feature = y et secat = 0.5?

50.8% Signup and view all the answers

Quid obstat ad accuratum decisionem in decision stump?

Data defectum Signup and view all the answers

Quod est secare magis efficax in data continuis?

secare = 0.2 Signup and view all the answers

Quod realmente adiuvare potest ad accuratum exitus in decision stump?

Combinatio varios factores Signup and view all the answers

Quomodo describitur accuratus output in decision stump continuis?

Medium ex 0-1 damno Signup and view all the answers

Quid necessario non est ad parametra optimanda in decision stump?

Genus pretiosa Signup and view all the answers

Quis est exactus modus pro calculando accuratum exitum?

Intermedia 0-1 damnum Signup and view all the answers

Quod elementum non pertinet ad decision stump continuis?

Translatio spatialis Signup and view all the answers

Quae de labelis in processu notandi verum est?

Labela pretiosa difficile obtinentur. Signup and view all the answers

Quid est significatio ‘semi-supervised’ in contextu discendi machinis?

Discendi cum quibusdam notis signatis et plerumque incognitis. Signup and view all the answers

Quid excluendum est in algorithmis supervisionibus?

Clustering. Signup and view all the answers

Quae ex sequentibus probas discendi qualitates est probabilistica?

60% casus catum invenire. Signup and view all the answers

Quid de notis ‘weakly-supervised’ est falsum?

Labela 'weak' ad individuum pertinent. Signup and view all the answers

Quid definit ‘active learning’?

Doctrinam cum datis collectis. Signup and view all the answers

Quod processus discendi in ‘batch learning’ non includit?

Discendum dum data perveniant. Signup and view all the answers

Quod affirmatio de modellis graphice est verum?

Possunt adhiberi in structura predictionis. Signup and view all the answers

Study Notes

Machine Learning 1.02: Decision Trees

Decision trees are a supervised classification algorithm.
They are easy to understand.
The next lecture will cover random forests, which are an extension of decision trees.
Random forests were a leading approach in machine learning before deep learning.

What is the goal? I

The goal is to learn a function, y = f(x), from data.
x is an n-dimensional feature vector.
y is an output class from {0, 1, ..., K-1} where K is the number of possible output classes.
This is a supervised machine learning problem.

What is the goal? II

The goal is to learn y = f(x) from data, specifically {(X1, Y1), (X2, Y2), ..., (XN, YN)}.
Choosing the best function, f, requires a loss function.

What is the goal? III

The task is to learn a function, y = f(x), from data. A set {(X1,Y1), ...,(XN,YN)}.
A suitable function f(x) , given a choice of parameter, should minimise the total loss calculated via a loss function L(yi, f(xi)).

Decision Stump I

An algorithm from a previous lecture is discussed, involving evaluating various features and matches to determine the best fit for the data. Parameters are discussed.

Decision Stump II

The algorithm from the previous lecture is reinterpreted in current notation, introducing parameters, functions including Kronecker delta function and a loss function.

Decision Stump III

The function space learned by the decision stump is explained.

Continuous decision stump I

The lecture discusses how to deal with continuous input features.
A function, f(x), is created for partitioning the space

Continuous decision stump II

Examples of continuous decision stumps for different splits are demonstrated with associated accuracy results.

Continuous decision stump III

Procedures are outlined to find optimal splits by sweeping parameters and evaluating loss functions

Continuous decision stump IV

The limitations of axis-aligned separation in real-world data are highlighted.

Decision trees I

Recursive splitting is introduced to improve upon the limitations of previous decision stumps.
The result can be viewed as a tree structure.

Decision trees II

The parameter of the function is a binary tree.
Two types of nodes are distinguished in the tree: internal nodes that execute the splits and leaf nodes containing the final answers.

Decision trees III

A method to evaluate the function fo(x) is explained through the use of the binary tree.

Decision trees IV

Explains Greedy optimisation of parameters (including brute force at each node), tree construction, and a way to avoid overfitting such as by using techniques such as Limiting the tree depth, Minimum leaf node size.

Decision trees V

The need for a loss function, L(y_i, f_{θ}(x_i)), is emphasized.
The 0-1 loss is unsuitable for decision trees but works well for decision stumps.

Gini Impurity

Gini impurity, a suitable loss function, is explained.
It measures the probability that two randomly selected items from the data set will have different classes.
The lowest value of Gini Impurity is 0.0, which implies that all items from the data set have the same class.
The highest value for Gini Impurity is less than 1.0, where each data point in the data set belongs to a different class.
A formula for calculating Gini impurity is presented.

Weighting the split

Explains how to weight the different splits to create a single number.

Information gain

Information gain measures the information gained by making a particular split in the data.
It is calculated via a formula involving Entropy.

Which loss function?

Comparison of the characteristics of Gini impurity and information gain as loss functions.
Gini impurity is faster to compute than information gain.

Overfitting

The concept of overfitting in decision trees is explained.
It occurs when a decision tree learns the details of the training data too well, including random noise.
The solution is discussed via Early Stopping.

Early stopping

Methods for avoiding overfitting by stopping early, including limiting tree depth and minimum leaf node size.
Prevents the decision tree function from becoming too complicated.
Hyperparameters are introduced as values that control these parameters.

A glossary of ML problem settings

A brief introduction to supervised machine learning, classification and regression.

Supervised learning: Classification

Defines supervised classification as learning a function y = f(x) with a discrete output, y.
Examples such as identifying camera trap animals from images, and predicting voting intention from demographic data are included.

Supervised learning: Regression

Defines supervised regression as learning a continuous function, y.
Provides examples such as predicting the critical temperature of a superconductor given material properties, and inferring particle paths in high energy physics experiments.

Supervised learning: Further kinds

Discusses multi-label classification, where the output variable y is a set (eg. identifying multiple objects in an image), structured prediction (eg. sentence tagging), and other cases where the output is structured.

Unsupervised learning

Describes unsupervised learning as finding patterns in data without any pre-defined outputs(or labels for the data).
Provides examples such as clustering (grouping similar data points), density estimation, and dimensionality reduction.

Unsupervised learning: Clustering

Describes grouping similar data points for various examples including finding co-regulated genes and identifying social groups
Includes real-world applications and diagrams.

Unsupervised learning: Dimensionality reduction

Explains dimensionality reduction as a method to reduce the number of variables of input data while preserving important information.

*supervised

Describes semi-supervised learning and weakly-supervised learning which use fewer labeled data points, but lots of unlabeled data (or cheap inaccurate data).
Introduces concepts from image recognition as an example.

Glossary

A summary of different types of classification, regression, and learning methodologies, including supervised, unsupervised and categories for methods.

Further categories

Describes different categorisation methods, for example, answering style (point versus probabilistic estimate) and workflow (eg. batch, incremental, and active learning).
Additional examples of categorisation by workflow or area (eg. computer vision or natural language processing (NLP)) are provided.

Summary

Describes the main topics covered in the presentation, as well as how the information has been presented.
Includes a summary of topics that this course may touch on further (eg., overfitting techniques).

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Description

Hic quiz explorat divisiones primarias arborum decisionis et eorum partes fundamentales. Discipuli discunt de effectibus divisionum et ratione adhibita ad determinandas regiones. Utilitatem divisionum secondarum in contextu arborum decisionis simul consideramus.