Analisis CART (Classification and Regression Trees) Pada Faktor-Faktor

Pola perkembangan kota dan urbanisasi yang pesat di kota–kota besar merupakan pertanda terjadinya kemajuan dalam pembangunan. Namun, Kenyataannya urbanisasi seringkali melahirkan berbagai masalah, mulai dari masalah sosial, transportasi, hingga kriminalitas. Hasil kajian BAPPENAS menunjukkan bahwa proyeksi tingkat urbanisasi penduduk secara nasional pada tahun 2025 mencapai 68%. Proyeksi tingkat urbanisasi Jawa Timur telah mencapai angka 73,4% pada tahun 2025, hal ini menunjukkan bahwa persentase penduduk perkotaan di Jawa Timur tergolong tinggi. Penelitian ini dilakukan untuk mendeskripsikan karakteristik kepala rumah tangga Jawa Timur yang melakukan urbanisasi dan mendapatkan faktor yang mempengaruhi urbanisasi. Hasil klasifikasi dengan pendekatan CART memberikan informasi bahwa faktor-faktor yang mempengaruhi urbanisasi adalah jarak lokasi pindah, jumlah anggota rumah tangga yang ikut pindah, pendidikan tertinggi, lama waktu pindah, alasan utama pindah

Penggunaan Metode Classification And Regression Trees (CART) untuk Klasifikasi Rekurensi Pasien Kanker Serviks di RSUD Dr. Soetomo Surabaya

Kanker serviks merupakan kanker yang menyerang area bawah rahim. Pengobatan kanker serviks tergantung pada besarnya ukuran dan stadium kanker. Kasus rekurensi sering terjadi walaupun pengobatan awal telah dilakukan. Salah satu rumah sakit yang menyediakan fasilitas terapi untuk pasien kanker serviks adalah RSUD dr. Soetomo Surabaya. Permasalahannya adalah bagaimana klasifikasi pasien kanker serviks di RSUD dr. Soetomo Surabaya yang rekuren dan tidak rekuren berdasarkan faktor-faktor yang mempengaruhi rekurensi kanker serviks dan mengetahui faktor-faktor yang berpengaruh terhadap rekurensi kanker serviks. Rekurensi yang dimaksudkan di penelitian ini adalah kembalinya pasien kanker serviks ke RSUD dr. Soetomo karena penyakit yang sama. Data yang digunakan merupakan data sekunder, yang diperoleh dari rekam medis pasien kanker serviks di RSUD dr. Soetomo Surabaya pada tahun 2014 dengan jumlah data sebanyak 810 pasien. Berdasarkan analisis yang telah dilakukan, pasien yang rekuren lebih banyak dibandingkan pasien yang tidak rekuren dengan persentase sebesar 57,78 persen untuk yang rekuren. Klasifikasi CART menghasilkan bahwa variabel yang paling berpengaruh terhadap rekurensi kanker serviks adalah variabel jenis pengobatan yang dijalani oleh pasien, selain itu variabel usia, status anemia dan status penyakit penyerta juga berpengaruh terhadap rekurensi kanker serviks. Ketepatan klasifikasi yang diperoleh untuk data prediksi sebesar 69,14 persen

evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees in R

Commonly used classification and regression tree methods like the CART algorithm are recursive partitioning methods that build the model in a forward stepwise search. Although this approach is known to be an efficient heuristic, the results of recursive tree methods are only locally optimal, as splits are chosen to maximize homogeneity at the next step only. An alternative way to search over the parameter space of trees is to use global optimization methods like evolutionary algorithms. This paper describes the "evtree" package, which implements an evolutionary algorithm for learning globally optimal classification and regression trees in R. Computationally intensive tasks are fully computed in C++ while the "partykit" (Hothorn and Zeileis 2011) package is leveraged for representing the resulting trees in R, providing unified infrastructure for summaries, visualizations, and predictions. "evtree" is compared to "rpart" (Therneau and Atkinson 1997), the open-source CART implementation, and conditional inference trees ("ctree", Hothorn, Hornik, and Zeileis 2006). The usefulness of "evtree" is illustrated in a textbook customer classification task and a benchmark study of predictive accuracy in which "evtree" achieved at least similar and most of the time better results compared to the recursive algorithms "rpart" and "ctree".machine learning, classification trees, regression trees, evolutionary algorithms, R

Bayesian classification and regression trees

The bachelor's thesis is devoted to classification and regression trees, their con- struction, and interpretation. In the first part, the reader gets acquainted with the structure of decision trees, basic definitions, and methodology. In the second part, more advanced and efficient methods for creating such trees using a Bayesian approach to the whole problem are presented. The last part of the work is focused on a practical task, where knowledge from this work is used. The entire text is accompanied by pictures, explanations, and derivations to make it easier for the reader to understand the whole problem in more depth. The thesis Bayesian classification and regression trees can serve all those interested who want to learn more about the issue of decision trees. 1Bakalářská práce se věnuje klasifikačním a regresním stromům, jejich stavbě a inter- pretaci. V první části se čtenář seznámí se strukturou rozhodovacích stromů, základními definicemi a metodikou. V druhé části jsou představeny pokročilejší a efektivnější metody pro tvorbu takových stromů využívající Bayesovský přístup k celému problému. Poslední část práce je zaměřená na praktickou úlohu, kde jsou využity poznatky z této práce. Celý text je doplněn obrázky, vysvětleními a odvozeními, aby bylo pro čtenáře jednodušší celý problém pochopit více do hloubky. Práce Bayesovské klasifikační a regresní stromy může posloužit všem zájemcům, kteří chtějí blíže poznat problematiku rozhodovacích stromů. 1Department of Probability and Mathematical StatisticsKatedra pravděpodobnosti a matematické statistikyFaculty of Mathematics and PhysicsMatematicko-fyzikální fakult

Discussion on Fifty Years of Classification and Regression Trees

In this discussion paper, we argue that the literature on tree algorithms is very fragmented. We identify possible causes and discuss good and bad sides of this situation. Among the latter is the lack of free open-source implementations for many algorithms. We argue that if the community adopts a standard of creating and sharing free open-source implementations for their developed algorithms and creates easy access to these programs the bad sides of the fragmentation will be actively combated and will benefit the whole scientific community. (authors' abstract

Exploiting informative priors for Bayesian classification and regression trees

A general method for defining informative priors on statistical models is presented and applied specifically to the space of classification and regression trees. A Bayesian approach to learning such models from data is taken, with the Metropolis- Hastings algorithm being used to approximately sample from the posterior. By only using proposal distributions closely tied to the prior, acceptance probabilities are easily computable via marginal likelihood ratios, whatever the prior used. Our approach is empirically tested by varying (i) the data, (ii) the prior and (iii) the proposal distribution. A comparison with related work is given

ANALISIS KLASIFIKASI NASABAH KREDIT MENGGUNAKAN BOOTSTRAP AGGREGATING CLASSIFICATION AND REGRESSION TREES (BAGGING CART)

Credit is one of the facilities provided by banks to lend money to someone or a business entity within the prescribed period. The smooth repayment of credit is essential for the bank because it influences the performance as well as its presence in daily life. Acceptance of prospective credit customers should be considered to minimize the occurrence of bad credit. Classification and Regression Trees (CART) is a statistical method that can be used to identify potency of credit customer status such as current credit and bad credit. The predictor variables used in this study are gender, age, marital status, number of children, occupation, income, tenor / period, and home ownership. To improve the stability and accuracy of the prediction were used the Bootstrap Aggregating Classification and Regression Trees (Bagging CART) method. The classification of credit customers using Bagging CART gives the classification accuracy 81,44%. Keywords : Credit, Bootstrap Aggregating Classification and Regression Trees (Bagging CART), Classification Accurac

Bayesian Classification and Regression Trees for Predicting Incidence of Cryptosporidiosis

Background Classification and regression tree (CART) models are tree-based exploratory data analysis methods which have been shown to be very useful in identifying and estimating complex hierarchical relationships in ecological and medical contexts. In this paper, a Bayesian CART model is described and applied to the problem of modelling the cryptosporidiosis infection in Queensland, Australia. Methodology/Principal Findings We compared the results of a Bayesian CART model with those obtained using a Bayesian spatial conditional autoregressive (CAR) model. Overall, the analyses indicated that the nature and magnitude of the effect estimates were similar for the two methods in this study, but the CART model more easily accommodated higher order interaction effects. Conclusions/Significance A Bayesian CART model for identification and estimation of the spatial distribution of disease risk is useful in monitoring and assessment of infectious diseases prevention and control