Download slides from here:https://drive.google.com/file/d/0BwkBn0oFDraSX2hIRTVVWXlnQlE/view?usp=sharin ما هي شجرة القرار؟ شجرة القرار (Decision Tree): طريقة تمثيل بصرية تعتمد على البيانات التوضيحية لتحديد مسار عملية اتخاذ القرار، وذلك من خلال وضع كل الاحتمالات الممكنة ونتيجتها. تعتبر شجرة القرار إحدى أدوات دعم القرار.

تعلم شجرة القرار (بالإنجليزية: Decision tree learning) من السهل شرح خوارزمية شجرة القرار. ينتج عنه مجموعة من القواعد. يتبع نفس النهج الذي يتبعه البشر بشكل عام أثناء اتخاذ القرارات ** هذا الفيديو صدقه جاريه علي روح جدي اتمني الدعاد له========================================للي محتاج ال pdf**. A decision tree is one of the simplest yet highly effective classification and prediction visual tools used for decision making. It takes a root problem or situation and explores all the possible scenarios related to it on the basis of numerous decisions. Since decision trees are highly resourceful, they play a crucial role in different sectors الوسم: Decision Tree شرح. (Decision Tree) هي رسم تخطيطي، على شكل شجرة متفرعة يستخدم لتحديد مسار العمل أو تظهر ما هي الاحتمالات الممكنة. يمثل كل فرع من فروع شجرة قرار محتمل. يتم تنظيم..

- شجرة القرارات. شجرة القرارات (Decision Tree) هي رسم تخطيطي، على شكل شجرة متفرعة يستخدم لتحديد مسار العمل أو تظهر ما هي الاحتمالات الممكنة. يمثل كل فرع من فروع شجرة قرار محتمل
- شجرة القرار هي هيكلية شبيهة بالمخطط الانسيابي حيث كل عقدة داخلية تمثل اختبار للخاصية (مثال هل الوجه الظاهر للعملة المعدنية هو طرة أم نقش)، كل فرع يمثل مخرجات الاختبار وكل عقدة نهائية تمثل القرار المتخذ بعد احتساب.
- خوارزمية ID3 Decision Tree. مقدمة: تتحدث المقالة عن احد خوارزميات تصنيف البيانات وهى خوارزمية ID3 ونستعرض فيها شرح للخوارزمية وكيفية تطبيقها والقصور فيها. تعريفها هى عبارة عن خوارزمية تستخدم لتصنيف.
- A decision tree is a map of the possible outcomes of a series of related choices. It allows an individual or organization to weigh possible actions against one another based on their costs, probabilities, and benefits. They can can be used either to drive informal discussion or to map out an algorithm that predicts the best choice mathematically
- Decision Tree Analysis is a general, predictive modelling tool that has applications spanning a number of different areas. In general, decision trees are constructed via an algorithmic approach that identifies ways to split a data set based on different conditions. It is one of the most widely used and practical methods for supervised learning
- ID3 (Iterative Dichotomiser) decision tree algorithm uses information gain. Mathematically, IG is represented as: In a much simpler way, we can conclude that: Information Gain. Where before is the dataset before the split, K is the number of subsets generated by the split, and (j, after) is subset j after the split

** A decision tree is a graph that uses a branching method to illustrate every pos-sible outcome of a decision**. It is powerful and popular tool for classi cation and prediction. Decision trees appeared in the literature in the context of sociological research in the sixties. In the eld of machine learning decision trees appeare Decision Tree is a tree-like graph where sorting starts from the root node to the leaf node until the target is achieved. It is the most popular one for decision and classification based on supervised algorithms. It is constructed by recursive partitioning where each node acts as a test case for some attributes and each edge, deriving from the. Decision tree is one of the most popular machine learning algorithms used all along, This story I wanna talk about it so let's get started!!! Decision trees are used for both classification an

- سنشرح هنا العديد من خوارزميات تعلم الآلة (machine learning) مثل التعلم العميق (deep learning) او التصنيف (classification) او التجميع (clustering) او شجرة اتخاذ القرار (decision tree) وهي مستخدمة في الذكاء الاصطناعي (artificial intelligence
- Decision Tree algorithm is one of the simplest yet powerful Supervised Machine Learning algorithms. Decision Tree algorithm can be used to solve both regression and classification problems in Machine Learning. That is why it is also known as CART or Classification and Regression Trees. As the name suggests, in Decision Tree, we form a tree-like.
- Decision tables and decision trees cannot always be used interchangeably within Pega Platform™ applications. You can reference a decision table or decision tree on flow rules, declare expressions, activities, or routers. Some configurations, such as cascading approvals with an authority matrix, only support evaluation of decision tables

Introduction to Decision Tree Algorithm. Decision Tree algorithm belongs to the family of supervised learning algorithms.Unlike other supervised learning algorithms, decision tree algorithm can be used for solving regression and classification problems too.. The general motive of using Decision Tree is to create a training model which can use to predict class or value of target variables by. season 1 episode 6: Decision Tree شرح من الاخر بالعربي لخوارزمات تعلم الالة ازاي تزرع شجرة برمجة. Random Forest models combine the simplicity of Decision Trees with the flexibility and power of an ensemble model.In a forest of trees, we forget about the high variance of an specific tree, and are less concerned about each individual element, so we can grow nicer, larger trees that have more predictive power than a pruned one

A decision tree is a supervised machine learning model used to predict a target by learning decision rules from features. As the name suggests, we can think of this model as breaking down our data by making a decision based on asking a series of questions. Let's consider the following example in which we use a decision tree to decide upon an activity on a particular day * Decision tree algorithm falls under the category of supervised learning*. They can be used to solve both regression and classification problems. Decision tree uses the tree representation to solve the problem in which each leaf node corresponds to a class label and attributes are represented on the internal node of the tree In decision tree learning, ID3 (Iterative Dichotomiser 3) is an algorithm invented by Ross Quinlan used to generate a decision tree from a dataset. ID3 is the precursor to the C4.5 algorithm, and is typically used in the machine learning and natural language processing domains درخت تصمیم (Decision Tree) یکی از ابزارها و تکنیکهایی است که در مهارتهای دادهکاوی بسیار پر کاربرد است. زمانی که حجم دادهها بسیار بالا باشد این تکنیک میتواند به کمک شما بیاید

شجرة القرار Decision Tree الإدارة والهندسة الصناعية. شجرة اتخاذ القرار Decision Tree شرح عربي شجرة اتخاذ القرار Smart3arabi A Decision Table Example. Table below is an illustration of a decision table developed using the steps previously outlined. In this example a company is trying to maintain a meaningful mailing list of customers. The objective is to send out only the catalogs from which customers will buy merchandise. The managers realize that certain loyal. Decision Tree : Decision tree is the most powerful and popular tool for classification and prediction.A Decision tree is a flowchart like tree structure, where each internal node denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (terminal node) holds a class label شرح تفصيلي و مبسط لبرمجة الاشجار و تعلم الالة Decision Tree Machine learning Decision Tree Machine learning Python script Decision Trees in Arabic source sourc

- Join this channel to get access to perks:https://www.youtube.com/channel/UCq3JMsTVMelj-vh3a4MFoxw/joi
- g that the -σ effect is the most probable explanation 25. Topliss Decision Tree 26. Topliss Decision Tree 27.
- Machine(Learning(for(Language(Technology((2016)(Lab02:$Decision$Trees$-$J48$ $ $ We(evaluate(the(performance(using(the(training(data,(which(has(beenloadedinth
- Schumacher, H. and Sevcik, K.C. (1976) The Synthetic Approach to Decision Table Conversion Comm. ACM Vol. 19 No. 6 (June 1976) p. 343-351 بوابة برمجة الحاسوب بوابة علم الحاسو
- مصطلحات. الرأس هو بنية تمتلك قيمة، شرط، أو تمثل هيكل بيانات منفصل (الذي يمكن أن يكون شجرة بحد ذاته). لكل رأس يوجد صفر أو أكثر من الرؤوس الأبناء، التي تكون تحته في الشجرة (بالإجماع، الشجر يرسم لأسفل)
- Decision Tree Classification Algorithm. Decision Tree is a Supervised learning technique that can be used for both classification and Regression problems, but mostly it is preferred for solving Classification problems. It is a tree-structured classifier, where internal nodes represent the features of a dataset, branches represent the decision rules and each leaf node represents the outcome

This decision tree does not cover all cases. For detailed information on the provision of text alternatives refer to the Image Concepts Page. Previous: Image Maps; Next: Tips and Tricks; We welcome your ideas. Please send any ideas, suggestions, or comments to the (publicly-archived) mailing list wai-eo-editors@w3.org The Decision Tree is an easily understood and interpreted algorithm and hence a single tree may not be enough for the model to learn the features from it. On the other hand, Random Forest is also a Tree-based algorithm that uses the qualities features of multiple Decision Trees for making decisions

Jan 13, 2020 - 1️⃣2️⃣الجزء Critical Control Point Decision Tree شرح شجرة إتخاذ القرار لتحديد نقاط التحكم الحرجة. Decision trees. The main concept behind decision tree learning is the following: starting from the training data, we will build a predictive model which is mapped to a tree structure. The goal is to achieve perfect classification with minimal number of decision, although not always possible due to noise or inconsistencies in data

3. So, this series of blog posts weren't't very much of a Choose Your Own Adventure™, with just one decision to be made, but I think The Antimatter Formula by Jay Leibold showed some of the advantages and disadvantages of Decision Trees and Decision Tables quite well.. For this example, a Decision Tree is probably best, since it is easier to read and follow Example decision tree of depth 2 for the Iris dataset. Decision trees are useful models because they allow a human to instantly visualize the decision-making process. However, in their basic form, they come with a large number of limitations, which are the reason why plain single decision trees are rarely used in machine learning today * بي تري ( بالإنجليزية: B-tree) في علوم الحاسب هي بيانات متسلسلة شجريا tree data structure , ومتوازنه ذاتيا Self-Balancing وهي تساعد على بقاء البيانات مفروزة sorted وتسمح بالبحث searches و والوصول المتسلسل sequential access*. Understanding a Decision Tree. A decision tree is the building block of a random forest and is an intuitive model. We can think of a decision tree as a series of yes/no questions asked about our data eventually leading to a predicted class (or continuous value in the case of regression) Decision trees are well researched, and relatively easy both to interpret and to implement; decision tree software is available for standard software packages [18, 33, 34] (Table 1). Like SVMs and neural networks, many methods for decision trees (e.g., ID3, C4.5) do not provide a probability of class membership although some variants, in.

Chapter 3 : Decision Tree Classifier — Coding. In this second part we try to explore sklearn library's decision tree classifier. We shall tune parameters discussed in theory part and checkout. Decision Tree. A decision tree is a verdict support tool that determines decisions by implying a tree-like and their probable consequences, together with possibility event outcomes, resource costs, etc. this technique allows them to display control statements that operate on conditional outcomes Season 1 Episode 8: كل ما تحتاج معرفتو عشان تعرف تدرب خوارزمات تعلم الالة و الذكاء الاصطناع في هذا الدرس شرح Hidden Markov Model نموذج ماركوف الخفي وهي احدى خوارزميات تعلم الآلة. وهي سلسة نمذجة لحالات منفصلة (modeling sequences with discrete states) تستخدم للتنبؤ باحتمال حصول حالة بناء على حالة سابقة Random forest is an ensemble of decision tree. Random forest helps avoid overfitting which is one of the key problem with decision tree classifier. For creating random forest, multiple trees are created using different sample sizes and features set. One of the key hyper parameter of random forest is number of trees represented using n_estimators

The process of adjusting Decision Tree to minimize misclassification error is called pruning. It is of 2 types prepruning and post pruning. SlideShare uses cookies to improve functionality and performance, and to provide you with relevant advertising 3. Decision Tree Algorithm. Decision Tree algorithms are used for both predictions as well as classification in machine learning. Using the decision tree with a given set of inputs, one can map the various outcomes that are a result of the consequences or decisions. We can understand decision trees with the following example The decision tree forms the structure shown above, calculating the best questions to ask in order to make the most accurate estimates possible. When we ask the decision tree to make a prediction for tomorrow , we must give it the same data it used during training (the features) and it gives us an estimate based on the structure it has learned Inductive bias refers to the restrictions that are imposed by the assumptions made in the learning method. For example, assuming that the solution to the problem of road safety can be expressed as a conjunction of a set of eight concepts

In this way, decision tree penalizes features that are not helpful in predicting the response variable (embedded method). After a tree has been made, there is an option to go back and 'prune' some of the nodes that do not provide any additional information to the model. This prevents overfitting, and is usually done through cross validation. The decision boundaries created by them is linear, but these can be much more complex than the decision tree because the many rules are triggered for the same record. An obvious question, which comes into the mind after knowing that the rules are not mutually exclusive is that how would the class be decided in case different rules with. مفاهيم أساسية عن التعلم المراقب للآلة Supervised Machine Learning Principles (20:19 Decision Tree Analysis \u0026 Decision Tree Example (Basic) Decision making شرح محاضرة How to cope with uncertainty Decision making under uncertainty - Maximin criteriaMaximax, Maximin, Hurwicz, Laplace, EMV 20. Uncertainty Decision Making Under Uncertainty and Mindfulness Part 2 Operations Research 11: Decision Trees \u0026 Decision. learners such as **decision** **trees** or splines. V arious choices of base-learner models are considered and described in the appropriate. section of this ar ticle. W e can now formulate the.

→ The weak learners in AdaBoost are decision trees with a single split, called decision stumps. → AdaBoost works b y putting more weight on difficult to classify instances and less on those already handled well. → AdaBoost algorithms can be used for both classification and regression problem. Part 1: Understanding AdaBoost using Decision. Prediction models are often presented as decision trees Decision Tree A decision tree is a support tool with a tree-like structure that models probable outcomes, cost of resources, utilities, and possible consequences. for choosing the best prediction. Gradient boosting presents model building in stages, just like other boosting methods, while. A decision tree model has high variance and low bias which can give us pretty unstable output unlike the commonly adopted logistic regression, which has high bias and low variance. That is the only point when Random Forest comes to the rescue. But before discussing Random Forest in detail, let's take a quick look at the tree concept شجرة اتخاذ القرار Decision Tree شرح عربي شجرة اتخاذ القرار Smart3arabi. Save Image. تحميل Snickometer Cricket Prediction Tool 1 1 Apk لـ Android. Save Image. علماء يطورون خوارزمية للتنبؤ بتفشي الأمراض مبكرا الحرة. Decision Tree. EMV is often used with Decision Trees, and it requires an appreciation of the concept of expected Value or Expected Monetary Value ─ a concept similar to Exposure. For example, imagine buying a sweepstake ticket for $1.00. There are two possible prizes: $100.00 and $10. 00. 5% of tickets payout $100. 0% payout $10

tree that resembles to an orientation diagram where each end node (leaf) is a decision (a class) and each non- final node (internal) represents a test. Each leaf represents the decision of belonging to a class of data verifying all tests path from the root to the leaf. The tree is simpler, and technically it seems easy to use. I Initially, as in AdaBoost's case. Very short decision trees were used, which had only one break, called the decision stump. Larger trees can typically be uses 4-to-8 stages. In particular, the number of layers, nodes, splits, or leaf nodes reduces. Additive Model. Trees are introduced one at a time. The current trees in the model are not updated

Boosting is a method of converting a set of weak learners into strong learners. AdaBoost, Gradient Boosting and XGBoost are three algorithms that do not get much recognition. The different types of boosting algorithms are: AdaBoost (Adaptive Boosting) AdaBoost works on improving the areas where the base learner fails The fifth season of One Tree Hill makes TV history, jumping ahead four years in the lives of its characters. Season 4 ended with the young characters graduating from high school, and Season 5 begins, post-college, four years later. Some of the characters have found success while others have Max Tree Depth (optional) أقصى عمق شجرة (اختياري) The maximum depth of each tree in the forest. Depth is another way of saying the number of rules each tree is allowed to create to come to a decision. Trees will not grow any deeper than this setting. أقصى عمق لكل شجرة في الغابة Muhammad ibn Abdullah (Arabic: مُحَمَّد بنِ عَبْد ٱللَّٰه, romanized: Muḥammad ibn ʿAbd Allāh Classical Arabic pronunciation: [muˈħammad]; c. 570 - 8 June 632 CE) was an Arab religious, social, and political leader and the founder of the world religion of Islam. According to Islamic doctrine, he was a prophet, divinely inspired to preach and confirm the.

- There are several approaches to avoiding overfitting in building decision trees. Pre-pruning that stop growing the tree earlier, before it perfectly classifies the training set. Post-pruning that allows the tree to perfectly classify the training set, and then post prune the tree
- The Data Science Lab. How to Create a Machine Learning Decision Tree Classifier Using C#. After earlier explaining how to compute disorder and split data in his exploration of machine learning decision tree classifiers, resident data scientist Dr. James McCaffrey of Microsoft Research now shows how to use the splitting and disorder code to create a working decision tree classifier
- أنشئ حسابًا جديدًا على Canva لتبدأ في تصاميمك لشجرة القرارات. اختر من مكتبتنا الزاخرة بقوالب مصممة باحترافية. قم بتنزيل صورك الخاصة، أو اختر من بين ما يزيد عن 1 مليون صورة مُخزنة. يمكنك معالجة.
- ويرد شرح لمختلف العناصر التي تم تقييمها في صنع القرار بمزيد من التفصيل في أماكن أخرى. Interactive Decision Tree Interactive decision tree
- شرح Ensemble learning Archives - IT Solutions. IT-Solutions In AI, article, line, tip, web-dev. تقنية الذكاء الصنعي بإختصار . ما هي تقنية الذكاء الصنعي التي يطلق عليها التعلم الجمعي أو الـ Ensemble learning ؟ (decision trees classifiers) والغابات.

١ توضيح طبيعة المشكلة. ٢ جمع البيانات وتلخيصها. ٣ إيجاد حلول بديلة للمشكلة. ٤ كتابة قائمة بأفضل الخيارات. ٥ اتخاذ القرار. ٦ تنفيذ القرار وتقييم نجاحه. ٧ مهارات اتخاذ القرار الصحيح. ٨ المراجع. Stochastic gradient boosting is an ensemble of decision trees algorithms. The stochastic aspect refers to the random subset of rows chosen from the training dataset used to construct trees, specifically the split points of trees. Stochastic Algorithm Behaviour. Because many machine learning algorithms make use of randomness, their nature (e.g. Any decision tree that sorts n elements has height (n lg n). Proof Consider a decision tree of height h that sorts n elements. Since there are n! permutations of n elements, each permutation representing a distinct sorted order, the tree must have at least n! leaves. Since a binary tree of height h has no more than 2 h leaves, we have. n! 2 h

Decision Trees: A Decision Tree is an algorithm that is used to visually represent decision-making. A Decision Tree can be made by asking a yes/no question and splitting the answer to lead to another decision. The question is at the node and it places the resulting decisions below at the leaves ** التنقيب في البيانات يشكل جزءا من اكتشاف المعرفة knowledge discovery، وهذه العملية هي الأكثر شمولا**. تتضمن عملية اكتشاف المعرفة الخطوات التالية: ١- اكتشاف البيانات Data discovery: وهي مرحلة جمع البيانات.

Decision tree: A decision tree performs the classification in the form of tree structure. It breaks down the dataset into small subsets and a decision tree can be designed simultaneously. The final result is a tree with decision node. For example: The following decision tree can be designed to declare a result, whether an applicant is eligible. This document explains what a problem analysis tree is, in which situations you can use it and how to conduct this kind of analysis. It also contains concrete case studies of problem trees. MDF (2005): MDF Tool: Problem Tree Analysis Module 2: Decision Trees In this module, we will discuss how to use decision trees to represent knowledge. The module concludes with a presentation of the Random Forest method that overcomes some of the limitations (such as high variance or low precision) of a single decision tree constructed from data Scikit-learn (Sklearn) is the most useful and robust library for machine learning in Python. It provides a selection of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction via a consistence interface in Python. This library, which is largely written in. The Best Guide On How To Implement Decision Tree In Python Lesson - 12. Random Forest Algorithm Lesson - 13. Understanding Naive Bayes Classifier Lesson - 14. The Best Guide to Confusion Matrix Lesson - 15. How to Leverage KNN Algorithm in Machine Learning? Lesson - 16. K-Means Clustering Algorithm: Applications, Types, Demos and Use Cases.

Initially, as in AdaBoost's case. Very short **decision** **trees** were used, which had only one break, called the **decision** stump. Larger **trees** can typically be uses 4-to-8 stages. In particular, the number of layers, nodes, splits, or leaf nodes reduces. Additive Model. **Trees** are introduced one at a time. The current **trees** in the model are not updated Many clustering algorithms work by computing the similarity between all pairs of examples. This means their runtime increases as the square of the number of examples n , denoted as O ( n 2) in complexity notation. O ( n 2) algorithms are not practical when the number of examples are in millions. This course focuses on the k-means algorithm. A baseline classification uses a naive classification rule such as : Base Rate (Accuracy of trivially predicting the most-frequent class). (The ZeroR Classifier in Weka) always classify to the largest clas Isolation Forest is based on the Decision Tree algorithm. It isolates the outliers by randomly selecting a feature from the given set of features and then randomly selecting a split value between the max and min values of that feature. This random partitioning of features will produce shorter paths in trees for the anomalous data points, thus.

Bagged decision trees have only one parameter: t t t, the number of trees. Random Forests have a second parameter that controls how many features to try when finding the best split . Our simple dataset for this tutorial only had 2 2 2 features ( x x x and y y y ), but most datasets will have far more (hundreds or thousands) Random forest is a combination of decision trees that can be modeled for prediction and behavior analysis. The decision tree in a forest cannot be pruned for sampling and hence, prediction selection. The random forest technique can handle large data sets due to its capability to work with many variables running to thousands Classification - decision tree Top-down induction of decision trees (TDIDT, old approach know from pattern recognition): • Select an attribute for root node and create a branch for each possible attribute value. • Split the instances into subsets (one for each branch extending from the node) Decision tree learning التعليم بواسطة شجرة القرار: من الأساليب المعروفة والمشهورة في علم البيانات ويتم استخدامها في عمليات التنبؤ prediction والتصنيف classification Naïve Bayes, which is computationally very efficient and easy to implement, is a learning algorithm frequently used in text classification problems. Two event models are commonly used: Multivariate Bernoulli Event Model. Multivariate Event Model. The Multivariate Event model is referred to as Multinomial Naive Bayes

A decision tree can be used to solve problems with discrete attributes as well as boolean functions. Some of the notable decision tree algorithms are ID3 and CART. 4. Random Forest Model. The random forest model is an ensemble method. It operates by constructing a multitude of decision trees and outputs a classification of the individual trees Types of Binary Trees (Based on Structure) Rooted binary tree: It has a root node and every node has atmost two children. Full binary tree: It is a tree in which every node in the tree has either 0 or 2 children. The number of nodes, n, in a full binary tree is atleast n = 2h - 1, and atmost n = 2 h+1 - 1, where h is the height of the tree. The number of leaf nodes l, in a full binary tree.

تترجم خدمة Google المجانية الكلمات والعبارات وصفحات الويب بين الإنجليزية وأكثر من 100 لغة أخرى Now, let us take a look at the different types of classifiers: Perceptron. Naive Bayes. Decision Tree. Logistic Regression. K-Nearest Neighbor. Artificial Neural Networks/Deep Learning. Support Vector Machine. Then there are the ensemble methods: Random Forest, Bagging, AdaBoost, etc Expression trees represent code in a tree-like data structure, where each node is an expression, for example, a method call or a binary operation such as x < y. You can compile and run code represented by expression trees

* sklearn*.ensemble.AdaBoostClassifier¶ class* sklearn*.ensemble. AdaBoostClassifier (base_estimator = None, *, n_estimators = 50, learning_rate = 1.0, algorithm = 'SAMME.R', random_state = None) [source] ¶. An AdaBoost classifier. An AdaBoost [1] classifier is a meta-estimator that begins by fitting a classifier on the original dataset and then fits additional copies of the classifier on the. 17 maj 2021 - Denna pin hittades av Najwa Husseiny Ozeir. Hitta (och spara!) dina egna pins på Pinterest

The decision-making process in business is the most important function of a manager as Peter F Drucker says that whatever a manager does, he does through decision making.. steps of the decision making process in business. Decision making is arriving at the conclusion whether to do certain things or not to do In true machine learning fashion, we'll ideally ask the machine to perform this exploration and select the optimal model architecture automatically. Parameters which define the model architecture are referred to as hyperparameters and thus this process of searching for the ideal model architecture is referred to as hyperparameter tuning مزيج الترشيح الفصل - Zondagskuil Safaris الفصل المكوس عنوان آلة انثي لقوة عالية مزيج الاسمنتالرجال أذكى من النساء.. حقيقة علمية أم وهم . 2017-6-7 إذن يتضح ل Classification is one of the most widely used techniques in machine learning, with a broad array of applications, including sentiment analysis, ad targeting, spam detection, risk assessment, medical diagnosis and image classification. The core goal of classification is to predict a category or class y from some inputs x بلکه با دسته بندی میلیونها مشتری به سه گروه شرح داده شده، به راحتی استراتژیکهای مختلفی را میتواند لحاظ کند. درخت تصمیم(Decision tree) ۶ بهمن, ۱۳۹

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