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?

<p>Iterat processum divisionis (C)</p> Signup and view all the answers

Quomodo arbor decisionis potest repraesentari?

<p>Ut diagramma (A)</p> Signup and view all the answers

Quae ratio adhibetur ad determinandum regiones in arboribus decisionis?

<p>Ratio puncta rubra et coerulea (B)</p> Signup and view all the answers

Quae est utilitas divisionis secundae in arboribus decisionis?

<p>Ampliat decisionem (C)</p> Signup and view all the answers

Quod est maximum caput informationis cum dividente?

<p>H(p (parent)) - H(p (left)) - H(p (right)) (A)</p> Signup and view all the answers

Quae est utilitas Gini impuritatis in computatione?

<p>Celeritas computationis, non utitur logaritmis (A)</p> Signup and view all the answers

In quibus condiciones informationis lucrum melius est?

<p>In problematicis regressionis (B)</p> Signup and view all the answers

Quot moduli impudentiae adhibentur in difficultate identitatis?

<p>Grafici minime varia, sed per exemplaria diversa (D)</p> Signup and view all the answers

Quid significat 'overfitting' in contextu data mining?

<p>Modellum nimis complexum pro notitia (B)</p> Signup and view all the answers

Quot sunt exemplaria maximi criterii informationis?

<p>Datis continuis et discretis (C)</p> Signup and view all the answers

Quod modum adhibere ubi Gini non sit maxime idoneus?

<p>In regressionibus (B)</p> Signup and view all the answers

Quam utilitas implicatur in quantitativa computatione Gini?

<p>Non adhibet logaritmos (A)</p> Signup and view all the answers

Quod argumentum Gini impuritatis describit probabilitatem?

<p>Probabilitas duorum item a data set seleccionatorum habere diversas classes. (D)</p> Signup and view all the answers

Quoties Gini impuritas aequalis est 0?

<p>Cum data set continet unam solam classem. (D)</p> Signup and view all the answers

Quis inter hoc non est pars functionis perditionis?

<p>Quadratum medio. (C)</p> Signup and view all the answers

Quid significat G(p) in contextu Gini impuritatis?

<p>Aestimatio probabilitatis duorum item diversarum classium. (B)</p> Signup and view all the answers

Quod indicat $p_i$ in formula pro Gini impuritate?

<p>Probabilitas selectionis classis i in data set. (D)</p> Signup and view all the answers

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

<p>Non potest specificare diversitatem classium. (D)</p> Signup and view all the answers

Quot classes necessariae sunt ad Gini impuritatem maximam?

<p>Omnes classes diversae. (D)</p> Signup and view all the answers

Quid est maximum value Gini impuritatis?

<p>Minor quam 1. (A)</p> Signup and view all the answers

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

<p>Quando omnes elementa sunt identica. (D)</p> Signup and view all the answers

Quod significat Gini impuritas cum in binario classificatione valet?

<p>Probabilitas selectionis diversorum classes. (B)</p> Signup and view all the answers

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

<p>Incipe ab vertice et move ad primam divisionem. (C)</p> Signup and view all the answers

Quid significat 'greedy optimisation' in constructione arborum?

<p>Optimum parametra in una solum divisione eligere. (D)</p> Signup and view all the answers

Quid fit si data in training solum unum genus continent?

<p>Generatur folium. (C)</p> Signup and view all the answers

Quod est objective ad selectionem optimarum subdivisionum?

<p>Minimam totam amissionem in partibus eligere. (A)</p> Signup and view all the answers

Quid est L(yi, fθ(xi))?

<p>Functionis amissionis necesse est. (A)</p> Signup and view all the answers

Quod genus amissionis non operatur bene in arboribus decisionum?

<p>Amissio 0-1. (A)</p> Signup and view all the answers

Quid significat 'recurse' in constructura arborum decisionum?

<p>Construens novas arborum structuras pro sinistris et dextris filiis. (D)</p> Signup and view all the answers

Quam adhibere ad decidendum quorum divider optimum?

<p>Per computando totam amissionem in omnibus divisionibus. (A)</p> Signup and view all the answers

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

<p>50.8% (C)</p> Signup and view all the answers

Quid indicat accuratum exitum 100% in continuis decision stump?

<p>Feature x, secat = 0.0 (C)</p> Signup and view all the answers

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

<p>83.2% (B)</p> Signup and view all the answers

Quod principium adhibetur ad spatium dividendum in decision stump continuis?

<p>Wenn xfeature &lt; split (D)</p> Signup and view all the answers

Quid significat accuratum exitum 0%?

<p>Tuus model non operatur. (A)</p> Signup and view all the answers

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

<p>50.8% (A)</p> Signup and view all the answers

Quid obstat ad accuratum decisionem in decision stump?

<p>Data defectum (C)</p> Signup and view all the answers

Quod est secare magis efficax in data continuis?

<p>secare = 0.2 (A)</p> Signup and view all the answers

Quod realmente adiuvare potest ad accuratum exitus in decision stump?

<p>Combinatio varios factores (A)</p> Signup and view all the answers

Quomodo describitur accuratus output in decision stump continuis?

<p>Medium ex 0-1 damno (D)</p> Signup and view all the answers

Quid necessario non est ad parametra optimanda in decision stump?

<p>Genus pretiosa (A)</p> Signup and view all the answers

Quis est exactus modus pro calculando accuratum exitum?

<p>Intermedia 0-1 damnum (D)</p> Signup and view all the answers

Quod elementum non pertinet ad decision stump continuis?

<p>Translatio spatialis (B)</p> Signup and view all the answers

Quae de labelis in processu notandi verum est?

<p>Labela pretiosa difficile obtinentur. (B)</p> Signup and view all the answers

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

<p>Discendi cum quibusdam notis signatis et plerumque incognitis. (D)</p> Signup and view all the answers

Quid excluendum est in algorithmis supervisionibus?

<p>Clustering. (D)</p> Signup and view all the answers

Quae ex sequentibus probas discendi qualitates est probabilistica?

<p>60% casus catum invenire. (A)</p> Signup and view all the answers

Quid de notis ‘weakly-supervised’ est falsum?

<p>Labela 'weak' ad individuum pertinent. (B)</p> Signup and view all the answers

Quid definit ‘active learning’?

<p>Doctrinam cum datis collectis. (B)</p> Signup and view all the answers

Quod processus discendi in ‘batch learning’ non includit?

<p>Discendum dum data perveniant. (D)</p> Signup and view all the answers

Quod affirmatio de modellis graphice est verum?

<p>Possunt adhiberi in structura predictionis. (C)</p> Signup and view all the answers

Flashcards

Ratio of red to blue

Ratio of red to blue indicat color of a region

First split

First split: divide the area in half and then shade the left side using a red and blue point ratio

Recursive splitting

Each half split is recursively divided again

Decision tree

A tree-like diagram representing the process of repeated splitting and shading

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Left half split

The first split further divides the left half, creating smaller regions

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Recursive division

Dividing a region recursively until a desired level of detail is reached

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Shading based on ratio

Shading the entire region based on the ratio of red to blue points

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Decision tree process

The process of repeatedly splitting regions and shading them based on the red/blue ratio

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Decision stump

Decision stump: A simple decision tree with a single node and two leaves, used to make predictions based on splitting data along a single feature. The split point is selected to maximize accuracy, based on how well it separates data points into classes.

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Continuous decision stump

A type of decision stump designed to work with continuous data, where features can take on values within a range. Continuous decision stumps split the data based on a threshold value within a continuous feature.

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Accuracy

A measure of how accurate a model's predictions are. It's calculated as one minus the average 0-1 loss, where the loss is 1 for incorrect predictions and 0 for correct ones. Higher accuracy indicates better predictive performance.

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Partition

The process of dividing data based on a chosen feature. It is usually used to create groups that help in decision-making, especially in machine learning.

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Feature

The specific feature used for splitting data. In continuous decision stumps, a continuous feature like height or weight would be used to separate data into groups based on threshold values.

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Split

The value used to divide data points using a chosen feature. Setting a threshold for selecting groups within a feature.

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Finding Best Parameters

The process of finding the best parameters for a decision stump, maximizing the accuracy of the model's predictions. It involves experimenting with different split values and feature combinations to find the optimal configuration.

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Evaluation

This process evaluates the accuracy of a decision stump using different combinations of features and split values. It aims to find the best parameters that effectively separate data points into their correct classes.

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Average 0-1 Loss

The average 0-1 loss measures the average number of incorrect predictions made by a model. It is calculated as 1 for incorrect predictions and 0 for correct predictions, and the average loss is calculated across all predictions.

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Lucrum informationis

Mensura quantitatis informationis quae lucrata est ex divisione datae.

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Functio impuritatis

Functio quae definitur ut quantitas impuritatis in nodo datae.

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Impuritas Gini

Functio impuritatis quae mensurat probabilitatem ut duae electiones eadem classe sint.

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Divisio

Methodus divisionis datae in duos nodes, ubi singulum nodum continet data quae est similis.

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Entropia

Functio quae mensurat informationem in nodo datae.

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Lucrum informationis divisionis

Lucrum informationis cum divisione datae in duas partes.

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Divisio heterogena

Methodus divisionis datae in duos nodes, ubi singulum nodum continet data quae est dissimilis.

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Informationis quantitas

Functio quae mensurat informationem in datae.

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Decisionis arboris aestimatio

Functio fθ(x) aestimatio, a vertice arboris incipiens.

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Decisionis arbor

Arbor, in quo nodus singulus probationem facit, pro decisione de nodo sequenti.

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Decisionis arboris optimizatio avari

Aedificatio arboris decisionis, in quo optima divisione pro quolibet nodo eligimus.

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Nodulus folii

Nodus folii, qui predictionem reddit, quando pervenimus ad finem arboris.

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Recursionem arboris

Iterativus processus aedificandi arborem, in quo aedificamus subarbores pro quolibet nodo.

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Functio damni

Functio qua aestimatio arboris decisionis aestimatur per comparationem ad valorem verum.

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Damnum 0-1

Forma damni quod efficit ut arbor decisionis simpliciorem efficiat.

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Decisionis arboris damni 0-1 fallacia

Usus damni 0-1 pro arboribus decisionis complexis potest ad decisiones male correctas ducere.

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Gini impuritas

Functio quae minime differentiam inter duo elementa ex dato seriei probabilitatis demonstrat. Minimus numerus 0 est cum omnes elementa eandem classem habent, maximus numerus (minus quam 1) si unusquisque habes diversam classem.

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Lucri informatio

Unum ex duobus functionibus quae efficienter utilitatem scissionis in arbore decisionis aestimat. Alia functio est Gini impuritas.

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0-1 perditus

Functio quae in classificatione modelorum usum est, quae 0 pro veris predictionibus et 1 pro predictionibus falsis valet. Media valuum 0-1 perdita statuit precisionem modelli.

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Processus arboris decisionis

Processus cum multis partibus in diversis divisis et shading, cum ratione punctorum rubrorum ad caeruleos ut criterium usurpantis, ad regionem in arbore decisionis definiendi.

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Data inscripta

Data, quam in machinam discendam adhibetur, est inscripta cum notitia de classe quam adhaeret.

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Data non inscripta

Data, quam in machinam discendam adhibetur, est non inscripta cum notitia de classe, sed habet notitiam de variabilibus.

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Supervisio

Ratio, qua data inscripta adhibetur ut machinam discendam tradetur, est supervisio.

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Non supervisio

Ratio, qua data non inscripta adhibetur ut machinam discendam tradetur, est non supervisio.

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Semi-supervisio

Ratio, qua data inscripta et data non inscripta adhibetur ut machinam discendam tradetur, est semi-supervisio.

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Debiliter supervisio

Ratio, qua data inscripta imperfecta adhibetur ut machinam discendam tradetur, est debiliter supervisio.

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Classificatio algorithmorum

Machinae discendae algorithmi possunt classificari secus quam per data.

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Qualitas Responsionis

Machinae discendae algorithmi possunt classificari secundum qualitatem responsionis.

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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.

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