Decision trees can be used for both classification and regression problems, but are mostly used for classification. They use a tree structure with internal nodes representing features, branches representing decision rules, and leaf nodes representing outcomes. The CART algorithm is commonly used to build decision trees by recursively splitting the dataset into purer child nodes based on feature tests.
Gradient descent is an iterative optimization algorithm used to find the minimum of a loss function. It works by taking steps in the direction of the negative gradient of the function to minimize it. Gradient descent requires the loss function to be differentiable and convex. It calculates the gradient, or slope of the curve, at each point to determine how to update the parameters to reduce the loss.
Machine Learning Unit-5 Decesion Trees & Random Forest.pdfAdityaSoraut
Its all about Machine learning .Machine learning is a field of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that enable computers to perform tasks without explicit programming instructions. Instead, these algorithms learn from data, identifying patterns, and making decisions or predictions based on that data.
There are several types of machine learning approaches, including:
Supervised Learning: In this approach, the algorithm learns from labeled data, where each example is paired with a label or outcome. The algorithm aims to learn a mapping from inputs to outputs, such as classifying emails as spam or not spam.
Unsupervised Learning: Here, the algorithm learns from unlabeled data, seeking to find hidden patterns or structures within the data. Clustering algorithms, for instance, group similar data points together without any predefined labels.
Semi-Supervised Learning: This approach combines elements of supervised and unsupervised learning, typically by using a small amount of labeled data along with a large amount of unlabeled data to improve learning accuracy.
Reinforcement Learning: This paradigm involves an agent learning to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties, enabling it to learn the optimal behavior to maximize cumulative rewards over time.Machine learning algorithms can be applied to a wide range of tasks, including:
Classification: Assigning inputs to one of several categories. For example, classifying whether an email is spam or not.
Regression: Predicting a continuous value based on input features. For instance, predicting house prices based on features like square footage and location.
Clustering: Grouping similar data points together based on their characteristics.
Dimensionality Reduction: Reducing the number of input variables to simplify analysis and improve computational efficiency.
Recommendation Systems: Predicting user preferences and suggesting items or actions accordingly.
Natural Language Processing (NLP): Analyzing and generating human language text, enabling tasks like sentiment analysis, machine translation, and text summarization.
Machine learning has numerous applications across various domains, including healthcare, finance, marketing, cybersecurity, and more. It continues to be an area of active research and
This presentation educate you about Decision Tree, Decision Tree Algorithm, Types of Decision Trees with example, Important Terminology related to Decision
Trees, Assumptions while creating Decision Tree.
For more topics stay tuned with Learnbay.
Understanding Decision Trees in Machine Learning: A Comprehensive Guidecyberprosocial
In the realm of machine learning, decision trees stand as fundamental tools for data analysis and predictive modeling. Their intuitive structure and robust capabilities make them a cornerstone in various fields, from finance to healthcare to marketing. In this article, we’ll delve into its essence, exploring its definition, components, applications, and significance in the realm of machine learning.
Machine Learning Unit-5 Decesion Trees & Random Forest.pdfAdityaSoraut
Its all about Machine learning .Machine learning is a field of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that enable computers to perform tasks without explicit programming instructions. Instead, these algorithms learn from data, identifying patterns, and making decisions or predictions based on that data.
There are several types of machine learning approaches, including:
Supervised Learning: In this approach, the algorithm learns from labeled data, where each example is paired with a label or outcome. The algorithm aims to learn a mapping from inputs to outputs, such as classifying emails as spam or not spam.
Unsupervised Learning: Here, the algorithm learns from unlabeled data, seeking to find hidden patterns or structures within the data. Clustering algorithms, for instance, group similar data points together without any predefined labels.
Semi-Supervised Learning: This approach combines elements of supervised and unsupervised learning, typically by using a small amount of labeled data along with a large amount of unlabeled data to improve learning accuracy.
Reinforcement Learning: This paradigm involves an agent learning to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties, enabling it to learn the optimal behavior to maximize cumulative rewards over time.Machine learning algorithms can be applied to a wide range of tasks, including:
Classification: Assigning inputs to one of several categories. For example, classifying whether an email is spam or not.
Regression: Predicting a continuous value based on input features. For instance, predicting house prices based on features like square footage and location.
Clustering: Grouping similar data points together based on their characteristics.
Dimensionality Reduction: Reducing the number of input variables to simplify analysis and improve computational efficiency.
Recommendation Systems: Predicting user preferences and suggesting items or actions accordingly.
Natural Language Processing (NLP): Analyzing and generating human language text, enabling tasks like sentiment analysis, machine translation, and text summarization.
Machine learning has numerous applications across various domains, including healthcare, finance, marketing, cybersecurity, and more. It continues to be an area of active research and
This presentation educate you about Decision Tree, Decision Tree Algorithm, Types of Decision Trees with example, Important Terminology related to Decision
Trees, Assumptions while creating Decision Tree.
For more topics stay tuned with Learnbay.
Understanding Decision Trees in Machine Learning: A Comprehensive Guidecyberprosocial
In the realm of machine learning, decision trees stand as fundamental tools for data analysis and predictive modeling. Their intuitive structure and robust capabilities make them a cornerstone in various fields, from finance to healthcare to marketing. In this article, we’ll delve into its essence, exploring its definition, components, applications, and significance in the realm of machine learning.
Get to know in detail the termonologies of Random Forest with their types of algorithms used in the workflow along with their advantages and disadvantages of their predecessors.
Thanks, for your time, if you enjoyed this short article there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
DCOM (Distributed Component Object Model) and CORBA (Common Object Request Broker Architecture) are two popular distributed object models. In this paper, we make architectural comparison of DCOM and CORBA at three different layers: basic programming architecture, remoting architecture, and the wire protocol architecture.
Decision Tree & Decision Table
Data Flow Diagram (DFD)
Data Dictionary (DD)
Element of DD
Advantages & Disadvantages of DD
Input/Output Design
Pseudo Code
Case Studies on Above Topic
Data Science - Part V - Decision Trees & Random Forests Derek Kane
This lecture provides an overview of decision tree machine learning algorithms and random forest ensemble techniques. The practical example includes diagnosing Type II diabetes and evaluating customer churn in the telecommunication industry.
Scalable decision tree based on fuzzy partitioning and an incremental approachIJECEIAES
Classification as a data mining materiel is the process of assigning entities to an already defined class by examining the features. The most significant feature of a decision tree as a classification method is its ability to data recursive partitioning. To choose the best attributes for partition, the value range of each continuous attribute should be divided into two or more intervals. Fuzzy partitioning can be used to reduce noise sensitivity and increase the stability of trees. Also, decision trees constructed with existing approaches, tend to be complex, and consequently are difficult to use in practical applications. In this article, a fuzzy decision tree has been introduced that tackles the problem of tree complexity and memory limitation by incrementally inserting data sets into the tree. Membership functions are generated automatically. Then fuzzy information gain is used as a fast-splitting attribute selection criterion and the expansion of a leaf is done attending only with the instances stored in it. The efficiency of this algorithm is examined in terms of accuracy and tree complexity. The results show that the proposed algorithm by reducing the complexity of the tree can overcome the memory limitation and make a balance between accuracy and complexity.
The decision tree is one of the topics of the Bigdata analytics which is a subject of 8th sem CSE students. Book referred is data analytics by Anil Maheshwari.
Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
It has a tough exterior. Despite its limitations and drawbacks, decision trees are still effective at splitting data and creating predictive models.You can learn more about ML by joining Machine Learning Coaching In Bangalore by Tutort Academy.
An Introduction to Random Forest and linear regression algorithmsShouvic Banik0139
This presentation aims to provide a comprehensive understanding of the Random Forest and Linear Regression algorithms, their functioning, and significance. It is designed to equip the audience with the knowledge required to apply these algorithms effectively in practical scenarios, and to further enhance their expertise in the field.
Get to know in detail the termonologies of Random Forest with their types of algorithms used in the workflow along with their advantages and disadvantages of their predecessors.
Thanks, for your time, if you enjoyed this short article there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
DCOM (Distributed Component Object Model) and CORBA (Common Object Request Broker Architecture) are two popular distributed object models. In this paper, we make architectural comparison of DCOM and CORBA at three different layers: basic programming architecture, remoting architecture, and the wire protocol architecture.
Decision Tree & Decision Table
Data Flow Diagram (DFD)
Data Dictionary (DD)
Element of DD
Advantages & Disadvantages of DD
Input/Output Design
Pseudo Code
Case Studies on Above Topic
Data Science - Part V - Decision Trees & Random Forests Derek Kane
This lecture provides an overview of decision tree machine learning algorithms and random forest ensemble techniques. The practical example includes diagnosing Type II diabetes and evaluating customer churn in the telecommunication industry.
Scalable decision tree based on fuzzy partitioning and an incremental approachIJECEIAES
Classification as a data mining materiel is the process of assigning entities to an already defined class by examining the features. The most significant feature of a decision tree as a classification method is its ability to data recursive partitioning. To choose the best attributes for partition, the value range of each continuous attribute should be divided into two or more intervals. Fuzzy partitioning can be used to reduce noise sensitivity and increase the stability of trees. Also, decision trees constructed with existing approaches, tend to be complex, and consequently are difficult to use in practical applications. In this article, a fuzzy decision tree has been introduced that tackles the problem of tree complexity and memory limitation by incrementally inserting data sets into the tree. Membership functions are generated automatically. Then fuzzy information gain is used as a fast-splitting attribute selection criterion and the expansion of a leaf is done attending only with the instances stored in it. The efficiency of this algorithm is examined in terms of accuracy and tree complexity. The results show that the proposed algorithm by reducing the complexity of the tree can overcome the memory limitation and make a balance between accuracy and complexity.
The decision tree is one of the topics of the Bigdata analytics which is a subject of 8th sem CSE students. Book referred is data analytics by Anil Maheshwari.
Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
It has a tough exterior. Despite its limitations and drawbacks, decision trees are still effective at splitting data and creating predictive models.You can learn more about ML by joining Machine Learning Coaching In Bangalore by Tutort Academy.
An Introduction to Random Forest and linear regression algorithmsShouvic Banik0139
This presentation aims to provide a comprehensive understanding of the Random Forest and Linear Regression algorithms, their functioning, and significance. It is designed to equip the audience with the knowledge required to apply these algorithms effectively in practical scenarios, and to further enhance their expertise in the field.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
1. NADAR SARASWATHI COLLEGE OF ARTS AND SCIENCE
Classification tree and gradient descent algorithm
P.Gayathri
II M.Sc ComputerScience
ARTIFICIALINTELLIGENCEANDMACHINELEARNING
2. 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.
3. In a Decision tree, there are two nodes, which are
the Decision Node and Leaf Node. Decision nodes are
used to make any decision and have multiple branches,
whereas Leaf nodes are the output of those decisions
and do not contain any further branches.
The decisions or the test are performed on the basis of
features of the given dataset.
4. It is a graphical representation for getting all the
possible solutions to a problem/decision based on
given conditions.
It is called a decision tree because, similar to a tree, it
starts with the root node, which expands on further
branches and constructs a tree-like structure.
5. In order to build a tree, we use the CART
algorithm, which stands for Classification and
Regression Tree algorithm.
A decision tree simply asks a question, and based on the
answer (Yes/No), it further split the tree into subtrees.
Below diagram explains the general structure of a decision
tree:
6.
7. Decision Tree Terminologies
•Root Node: Root node is from where the decision tree starts. It represents the entire dataset, which
further gets divided into two or more homogeneous sets.
•Leaf Node: Leaf nodes are the final output node, and the tree cannot be segregated further after
getting a leaf node.
•Splitting: Splitting is the process of dividing the decision node/root node into sub-nodes according to
the given conditions.
•Branch/Sub Tree: A tree formed by splitting the tree.
•Pruning: Pruning is the process of removing the unwanted branches from the tree.
•Parent/Child node: The root node of the tree is called the parent node, and other nodes are called
the child nodes.
8. Gradient descent (GD) is an iterative first-order
optimisation algorithm, used to find a local
minimum/maximum of a given function. This method
is commonly used in machine learning (ML) and deep
learning (DL) to minimise a cost/loss function (e.g. in a
linear regression).
9. However, its use is not limited to ML/DL only,
it’s widely used also in areas like:
control engineering (robotics, chemical, etc.)
computer games
mechanical engineering
10. Gradient descent algorithm does not work for all
functions. There are two specific requirements. A
function has to be:
differentiable
convex
11. Gradient
Before jumping into code one more thing has to be explained —
what is a gradient. Intuitively it is a slope of a curve at a given point
in a specified direction.
In the case of a univariate function, it is simply the first
derivative at a selected point. In the case of a multivariate
function, it is a vector of derivatives in each main direction (along
variable axes). Because we are interested only in a slope along one
axis and we don’t care about others these derivatives are
called partial derivatives.