Decision tree regression

Artificial neural networks, which are an essential tool in Machine Learning, are used to solve many types of problems in different fields. This article will introduce an application of the artificial neural network model in the diagnosis of heart disease based on the heart.csv data file.

6p viengfa 28-10-2024 1 1   Download

In this study, we use various features, including permission lists, API system calls, and library lists, to create a system named AnLibsXAI to classify Android malware. We experimented with six machine learning models such as Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN) and Multilayer Perceptron (MLP).

12p viyoko 01-10-2024 2 1   Download

Luận văn "Sử dụng Data Mining dự báo nhu cầu lao động cho một số ngành nghề trên địa bàn tỉnh Bình Dương" được hoàn thành với mục tiêu nhằm nghiên cứu về ứng dụng khai phá dữ liệu và các thuật toán Linear Regression, K-nearest neighbors, Decision trees và Random forests để khai phá dữ liệu cho Dữ liệu tại Trung tâm dịch vụ việc làm tỉnh Bình Dương với một cơ sở dữ liệu điều tra về cầu lao động của các Doanh nghiệp trên địa bàn tỉnh Bình Dương.

67p matroinho2510 08-11-2022 14 4   Download

Bài giảng Cây quyết định trong máy học cung cấp cho người học những kiến thức như: Position Salaries, Tiền xử lý dữ liệu; Trực quan hóa dữ liệu; Decision Tree; Decision Tree Regression; Huấn luyện mô hình;...Mời các bạn cùng tham khảo!

29p toan5ks1 04-08-2021 33 2   Download

Bài viết trình bày kết quả đánh giá bộ cơ sở dữ liệu trong phân loại rối loạn phổ tự kỷ (ASD) trẻ em trên kho dữ liệu UCI. Chúng tôi tiến hành đánh giá bộ dữ liệu với các thuật toán SVM và Random Forest, đồng thời khảo sát thêm các thuật toán Decision Trees, Logistic Regression, K-Nearest-Neighbors, Naïve Bayes, và mạng nơ-ron Multi Layer Perceptron (MLP).

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What are anomalies/outliers? The set of data points that are considerably different than the remainder of the data Variants of Anomaly/Outlier Detection Problems Given a database D, find all the data points x  D with anomaly scores greater than some threshold t Given a database D, find all the data points x  D having the top-n largest anomaly scores f(x) Given a database D, containing mostly normal (but unlabeled) data points, and a test point x, compute the anomaly score of x with respect to D Applications: Credit card fraud detection, telecommunication fraud detection, network i...

25p trinh02 18-01-2013 71 5   Download

Creates nested clusters Agglomerative clustering algorithms vary in terms of how the proximity of two clusters are computed MIN (single link): susceptible to noise/outliers MAX/GROUP AVERAGE:  may not work well with non-globular clusters CURE algorithm tries to handle both problems Often starts with a proximity matrix A type of graph-based algorithm

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Finding groups of objects such that the objects in a group will be similar (or related) to one another and different from (or unrelated to) the objects in other groups. Understanding Group related documents for browsing, group genes and proteins that have similar functionality, or group stocks with similar price fluctuations Summarization Reduce the size of large data sets

104p trinh02 18-01-2013 59 7   Download

Transform categorical attribute into asymmetric binary variables Introduce a new “item” for each distinct attribute-value pair Example: replace Browser Type attribute with Browser Type = Internet Explorer Browser Type = Mozilla Browser Type = Mozilla

67p trinh02 18-01-2013 63 6   Download

Given a set of transactions, find rules that will predict the occurrence of an item based on the occurrences of other items in the transaction. Given a set of transactions T, the goal of association rule mining is to find all rules having support ≥ minsup threshold confidence ≥ minconf threshold Brute-force approach: List all possible association rules Compute the support and confidence for each rule Prune rules that fail the minsup and minconf thresholds  Computationally prohibitive!...

82p trinh02 18-01-2013 75 5   Download

Mutually exclusive rules Classifier contains mutually exclusive rules if the rules are independent of each other Every record is covered by at most one rule Exhaustive rules Classifier has exhaustive coverage if it accounts for every possible combination of attribute values Each record is covered by at least one rule

90p trinh02 18-01-2013 61 5   Download

Given a collection of records (training set ) Each record contains a set of attributes, one of the attributes is the class. Find a model for class attribute as a function of the values of other attributes. Goal: previously unseen records should be assigned a class as accurately as possible. A test set is used to determine the accuracy of the model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it.

101p trinh02 18-01-2013 104 13   Download

Key motivations of data exploration include Helping to select the right tool for preprocessing or analysis Making use of humans’ abilities to recognize patterns People can recognize patterns not captured by data analysis tools  Related to the area of Exploratory Data Analysis (EDA) Created by statistician John Tukey Seminal book is Exploratory Data Analysis by Tukey A nice online introduction can be found in Chapter 1 of the NIST Engineering Statistics Handbook http://www.itl.nist.gov/div898/handbook/index.htm...

41p trinh02 18-01-2013 72 6   Download

Collection of data objects and their attributes An attribute is a property or characteristic of an object Examples: eye color of a person, temperature, etc. Attribute is also known as variable, field, characteristic, or feature A collection of attributes describe an object Object is also known as record, point, case, sample, entity, or instance

68p trinh02 18-01-2013 52 4   Download

Lots of data is being collected and warehoused Web data, e-commerce purchases at department/grocery stores Bank/Credit Card transactions Computers have become cheaper and more powerful Competitive Pressure is Strong Provide better, customized services for an edge (e.g. in Customer Relationship Management)

29p trinh02 18-01-2013 46 3   Download