The Decision Tree Algorithm Use in Supervised Machine Learning
نویسندگان
چکیده
Machine learning concept is handling the dataset and it used to analyze data recently machine highly demanding approach, because of that learn itself like human without explicitly programmed help make faster decision with this also handle computational by using various type algorithms, in paper only focus tree algorithm take quick once problem. predicts output which much faster. Key Words: learning, Supervised, Decision Tree, Regression.
منابع مشابه
The Alternating Decision Tree Learning Algorithm
The application of boosting procedures to de cision tree algorithms has been shown to pro duce very accurate classi ers These classi ers are in the form of a majority vote over a number of decision trees Unfortunately these classi ers are often large complex and di cult to interpret This paper describes a new type of classi cation rule the alternat ing decision tree which is a generalization of...
متن کاملدستهبندی داده‌های دورده‌ای با ابرمستطیل موازی محورهای مختصات
One of the machine learning tasks is supervised learning. In supervised learning we infer a function from labeled training data. The goal of supervised learning algorithms is learning a good hypothesis that minimizes the sum of the errors. A wide range of supervised algorithms is available such as decision tress, SVM, and KNN methods. In this paper we focus on decision tree algorithms. When we ...
متن کاملA Fast Decision Tree Learning Algorithm
There is growing interest in scaling up the widely-used decision-tree learning algorithms to very large data sets. Although numerous diverse techniques have been proposed, a fast tree-growing algorithm without substantial decrease in accuracy and substantial increase in space complexity is essential. In this paper, we present a novel, fast decision-tree learning algorithm that is based on a con...
متن کاملDecision making via semi-supervised machine learning techniques
Semi-supervised learning (SSL) is a class of supervised learning tasks and techniques that also exploits the unlabeled data for training. SSL significantly reduces labeling related costs and is able to handle large data sets. The primary objective is the extraction of robust inference rules. Decision support systems (DSSs) who utilize SSL have significant advantages. Only a small amount of labe...
متن کاملUse of Unlabeled Data in Supervised Machine Learning
In many machine learning problem domains large amounts of data are available but the cost of correctly labeling it prohibits its use. This paper presents a short overview of methods for using a small set of labeled data together with a large supplementary unlabeled dataset in order to learn a better hypothesis than just by using the labeled information.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Indian Scientific Journal Of Research In Engineering And Management
سال: 2023
ISSN: ['2582-3930']
DOI: https://doi.org/10.55041/ijsrem24665