ppt on decisions tree descisiontrees-1810518 (1).pptx
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Decision trees in Machine Learning
Supervised learning techniques , Decision tree algorithms for Machine learning . Classification and Regression trees.
ID3 Algorithm & ROC Analysis
The document discusses decision trees and the ID3 algorithm. It provides an overview of decision trees, describing their structure and how they are used for classification. It then explains the ID3 algorithm, which builds decision trees based on entropy and information gain. The key steps of ID3 are outlined, including calculating entropy and information gain to select the best attributes to split the data on at each node. Pros and cons of ID3 are also summarized. An example applying ID3 to classify characters from The Simpsons is shown.
Classification (ML).ppt
Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
IBM has a rich history with machine learning. One of its own, Arthur Samuel, is credited for coining the term, “machine learning” with his research (link resides outside ibm.com) around the game of checkers. Robert Nealey, the self-proclaimed checkers master, played the game on an IBM 7094 computer in 1962, and he lost to the computer. Compared to what can be done today, this feat seems trivial, but it’s considered a major milestone in the field of artificial intelligence.
Over the last couple of decades, the technological advances in storage and processing power have enabled some innovative products based on machine learning, such as Netflix’s recommendation engine and self-driving cars.
Machine learning is an important component of the growing field of data science. Through the use of statistical methods, algorithms are trained to make classifications or predictions, and to uncover key insights in data mining projects. These insights subsequently drive decision making within applications and businesses, ideally impacting key growth metrics. As big data continues to expand and grow, the market demand for data scientists will increase. They will be required to help identify the most relevant business questions and the data to answer them.
Machine learning algorithms are typically created using frameworks that accelerate solution development, such as TensorFlow and PyTorch.
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Machine Learning vs. Deep Learning vs. Neural Networks
Since deep learning and machine learning tend to be used interchangeably, it’s worth noting the nuances between the two. Machine learning, deep learning, and neural networks are all sub-fields of artificial intelligence. However, neural networks is actually a sub-field of machine learning, and deep learning is a sub-field of neural networks.
The way in which deep learning and machine learning differ is in how each algorithm learns. "Deep" machine learning can use labeled datasets, also known as supervised learning, to inform its algorithm, but it doesn’t necessarily require a labeled dataset. Deep learning can ingest unstructured data in its raw form (e.g., text or images), and it can automatically determine the set of features which distinguish different categories of data from one another. This eliminates some of the human intervention required and enables the use of larger data sets. You can think of deep learning as "scalable machine learning" as Lex Fridman notes in this MIT lecture (link resides outside ibm.com).
Classical, or "non-deep", machine learning is more dependent on human intervention to learn. Human experts determine the set of features to understand the differences between data inputs,
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This document discusses decision tree induction algorithms and their splitting criteria. It covers ID3, CART, and C4.5 algorithms. ID3 uses information gain and entropy for splitting criteria. CART uses the Gini index. The Gini index measures impurity at each node, with 0 being pure and 0.5 being completely impure. C4.5 improves on ID3 by using the gain ratio, which normalizes information gain to account for attributes with many values. The document provides examples of computing the Gini index and error for different distributions of data classes at nodes.
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The document discusses decision trees, including:
- The four main ingredients in constructing a decision tree: splitting rule, stopping rule, pruning rule, and prediction method.
- Common splitting rules use impurity functions like entropy and Gini impurity to choose the optimal split.
- Stopping and pruning rules help determine the size and complexity of the final tree model.
- Popular decision tree algorithms like CART and C5.0 are described along with their distinguishing features.
- Advantages of decision trees like interpretability are contrasted with disadvantages like potentially lower accuracy compared to other models.
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This document discusses classification techniques in data mining, including decision trees. It covers supervised vs. unsupervised learning, the classification process, decision tree induction using information gain and other measures, handling continuous attributes, overfitting, and tree pruning. Specific algorithms covered include ID3, C4.5, CART, and CHAID. The goal of classification and how decision trees are constructed from the training data is explained at a high level.
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The document discusses decision trees and the ID3 algorithm. It provides an overview of decision trees, describing their structure and how they are used for classification. It then explains the ID3 algorithm, which builds decision trees based on entropy and information gain. The key steps of ID3 are outlined, including calculating entropy and information gain to select the best attributes to split the data on at each node. Pros and cons of ID3 are also summarized. An example applying ID3 to classify characters from The Simpsons is shown.
Classification (ML).ppt
Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
IBM has a rich history with machine learning. One of its own, Arthur Samuel, is credited for coining the term, “machine learning” with his research (link resides outside ibm.com) around the game of checkers. Robert Nealey, the self-proclaimed checkers master, played the game on an IBM 7094 computer in 1962, and he lost to the computer. Compared to what can be done today, this feat seems trivial, but it’s considered a major milestone in the field of artificial intelligence.
Over the last couple of decades, the technological advances in storage and processing power have enabled some innovative products based on machine learning, such as Netflix’s recommendation engine and self-driving cars.
Machine learning is an important component of the growing field of data science. Through the use of statistical methods, algorithms are trained to make classifications or predictions, and to uncover key insights in data mining projects. These insights subsequently drive decision making within applications and businesses, ideally impacting key growth metrics. As big data continues to expand and grow, the market demand for data scientists will increase. They will be required to help identify the most relevant business questions and the data to answer them.
Machine learning algorithms are typically created using frameworks that accelerate solution development, such as TensorFlow and PyTorch.
Related content
Subscribe to IBM newsletters
Begin your journey to AI
Learn how to scale AI
Explore the AI Academy
Machine Learning vs. Deep Learning vs. Neural Networks
Since deep learning and machine learning tend to be used interchangeably, it’s worth noting the nuances between the two. Machine learning, deep learning, and neural networks are all sub-fields of artificial intelligence. However, neural networks is actually a sub-field of machine learning, and deep learning is a sub-field of neural networks.
The way in which deep learning and machine learning differ is in how each algorithm learns. "Deep" machine learning can use labeled datasets, also known as supervised learning, to inform its algorithm, but it doesn’t necessarily require a labeled dataset. Deep learning can ingest unstructured data in its raw form (e.g., text or images), and it can automatically determine the set of features which distinguish different categories of data from one another. This eliminates some of the human intervention required and enables the use of larger data sets. You can think of deep learning as "scalable machine learning" as Lex Fridman notes in this MIT lecture (link resides outside ibm.com).
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ML Decision Tree_2.pptx
This document discusses decision tree induction algorithms and their splitting criteria. It covers ID3, CART, and C4.5 algorithms. ID3 uses information gain and entropy for splitting criteria. CART uses the Gini index. The Gini index measures impurity at each node, with 0 being pure and 0.5 being completely impure. C4.5 improves on ID3 by using the gain ratio, which normalizes information gain to account for attributes with many values. The document provides examples of computing the Gini index and error for different distributions of data classes at nodes.
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The document discusses decision trees, including:
- The four main ingredients in constructing a decision tree: splitting rule, stopping rule, pruning rule, and prediction method.
- Common splitting rules use impurity functions like entropy and Gini impurity to choose the optimal split.
- Stopping and pruning rules help determine the size and complexity of the final tree model.
- Popular decision tree algorithms like CART and C5.0 are described along with their distinguishing features.
- Advantages of decision trees like interpretability are contrasted with disadvantages like potentially lower accuracy compared to other models.
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This document discusses classification techniques in data mining, including decision trees. It covers supervised vs. unsupervised learning, the classification process, decision tree induction using information gain and other measures, handling continuous attributes, overfitting, and tree pruning. Specific algorithms covered include ID3, C4.5, CART, and CHAID. The goal of classification and how decision trees are constructed from the training data is explained at a high level.
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This document discusses classification techniques for data mining. It covers supervised and unsupervised learning methods. Specifically, it describes classification as a two-step process involving model construction from training data and then using the model to classify new data. Several classification algorithms are covered, including decision tree induction, Bayes classification, and rule-based classification. Evaluation metrics like accuracy and techniques to improve classification like ensemble methods are also summarized.
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- The process of classification involves model construction using a training set and then applying the model to classify new data.
- Supervised learning aims to predict categorical labels while unsupervised learning groups data without labels.
- Popular classification algorithms covered include decision trees, Bayesian classification, and rule-based methods. Attribute selection measures, model evaluation, overfitting, and tree pruning are also discussed.
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Bayes Classification Methods,
Rule-Based Classification,
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Techniques to Improve Classification Accuracy: Ensemble Methods,
Summary
by
Jiawei Han, Micheline Kamber, and Jian Pei,
University of Illinois at Urbana-Champaign &
Simon Fraser University,
©2013 Han, Kamber & Pei. All rights reserved.
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This chapter discusses classification techniques for data mining, including decision trees, Bayes classification, and rule-based classification. It covers the basic process of classification, which involves constructing a model from training data and then using the model to classify new data. Decision tree induction and attribute selection measures like information gain, gain ratio, and Gini index are explained in detail. The chapter also discusses techniques for scaling up classification to large databases, addressing overfitting, and improving accuracy.
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The document summarizes key concepts in classification and decision tree induction. It discusses supervised vs unsupervised learning, the two-step classification process of model construction and usage, and decision tree induction basics including attribute selection measures like information gain, gain ratio, and Gini index. It also covers overfitting and techniques like prepruning and postpruning decision trees.
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This chapter discusses classification techniques for data mining. It begins with an overview of classification vs unsupervised learning and the basic process of classification which involves model construction using a training set and then using the model to classify new data. It then covers decision trees, describing the basic algorithm for inducing decision trees from data and different measures for attribute selection like information gain, gain ratio, and gini index. The chapter also discusses model evaluation, overfitting, tree pruning, and techniques for scaling classification to large databases.
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Insights about feature selection and the variable importance using gini/information gain and variance for regression.
Let me know if anything is required. Happy to help, Talk soon! #bobrupakroy
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Often a Machine Learning algorithm is seen as one of those magical weapons capable of revealing possible future scenarios to whoever holds it. In truth, it's a direct application of mathematical and statistical concepts, which sometimes generate complex models to be interpreted as output. However, there are predictive models based on decision trees that are really simple to understand. In this slide deck I'll explain what is behind a predictive model of this type.
Here the demo files: https://goo.gl/K6dgWC
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The document discusses classification techniques for supervised learning problems. It describes classification as predicting categorical class labels based on a training set of labeled data. The classification process involves constructing a model from the training set and then using the model to classify new unlabeled data. Common classification techniques discussed include decision tree induction, Bayesian classification methods, and rule-based classification. Model evaluation and techniques for improving accuracy, such as ensemble methods, are also covered.
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Machine learning is an area of AI concerned with automatic learning. Some ways ML can be used in expert systems include increasing inference efficiency, testing the knowledge base, and acquiring knowledge. The ID3 algorithm constructs a decision tree from a set of examples to derive production rules, aiming to find a small tree efficiently. It selects the attribute with the highest information gain at each node to minimize uncertainty. However, ID3 has limitations such as handling noise, continuous values, and verifying rules on a full dataset.
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Decision trees are a non-parametric hierarchical classification technique that can be represented using a configuration of nodes and edges. They are built using a greedy recursive algorithm that recursively splits training records into purer subsets based on splitting metrics like information gain or Gini impurity. Preventing overfitting involves techniques like pre-pruning by setting minimum thresholds or post-pruning by simplifying parts of the fully grown tree. Decision trees have strengths like interpretability but also weaknesses like finding only a local optimum and being prone to overfitting.
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This document discusses classification and prediction in machine learning. It defines classification as predicting categorical class labels, while prediction models continuous values. The key steps of classification are constructing a model from a training set and using the model to classify new data. Decision trees and rule-based classifiers are described as common classification methods. Attribute selection measures like information gain and gini index are explained for decision tree induction. The document also covers issues in data preparation and model evaluation for classification tasks.
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Decision trees are a machine learning technique that use a tree-like model to predict outcomes. They break down a dataset into smaller subsets based on attribute values. Decision trees evaluate attributes like outlook, temperature, humidity, and wind to determine the best predictor. The algorithm calculates information gain to determine which attribute best splits the data into the most homogeneous subsets. It selects the attribute with the highest information gain to place at the root node and then recursively builds the tree by splitting on subsequent attributes.
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Decision trees are a supervised learning algorithm that can be used for both classification and regression problems. They work by recursively splitting the data into purer subsets based on feature values, building a tree structure. Information gain is used to determine the optimal feature to split on at each node. Trees are constructed top-down by starting at the root node and finding the best split until reaching leaf nodes. Pruning techniques like pre-pruning and post-pruning can help reduce overfitting. While simple to understand and visualize, trees can be unstable and prone to overfitting.
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The presentation explains the decision tree and ensemble in machine learning.
I presented this at the Big data club for college students.
(Jan 31st, 2019)
2014-mo444-practical-assignment-04-paulo_faria
The document discusses applying machine learning techniques to identify compiler optimizations that impact program performance. It used classification trees to analyze a dataset containing runtime measurements for 19 programs compiled with different combinations of 45 LLVM optimizations. The trees identified optimizations like SROA and inlining that generally improved performance across programs. Analysis of individual programs found some variations, but also common optimizations like SROA and simplifying the control flow graph. Precision, accuracy, and AUC metrics were used to evaluate the trees' ability to classify optimizations for best runtime.
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5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
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7:个人信誉贷款加10分
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This document provides an overview of classification techniques in machine learning. It discusses:
- The process of classification involves model construction using a training set and then applying the model to classify new data.
- Supervised learning aims to predict categorical labels while unsupervised learning groups data without labels.
- Popular classification algorithms covered include decision trees, Bayesian classification, and rule-based methods. Attribute selection measures, model evaluation, overfitting, and tree pruning are also discussed.
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Slides contain:
Classification: Basic Concepts,
Decision Tree Induction,
Bayes Classification Methods,
Rule-Based Classification,
Model Evaluation and Selection,
Techniques to Improve Classification Accuracy: Ensemble Methods,
Summary
by
Jiawei Han, Micheline Kamber, and Jian Pei,
University of Illinois at Urbana-Champaign &
Simon Fraser University,
©2013 Han, Kamber & Pei. All rights reserved.
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This document discusses classification concepts and decision tree induction. It defines classification as predicting categorical class labels based on a training set. Decision tree induction is introduced as a basic classification algorithm that recursively partitions data based on attribute values to construct a tree. Information gain and the Gini index are presented as common measures for selecting the best attribute to use at each tree node split. Overfitting is identified as a potential issue, and prepruning and postpruning techniques are described to address it.
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The document summarizes key concepts in classification and decision tree induction. It discusses supervised vs unsupervised learning, the two-step classification process of model construction and usage, and decision tree induction basics including attribute selection measures like information gain, gain ratio, and Gini index. It also covers overfitting and techniques like prepruning and postpruning decision trees.
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Let me know if anything is required. Happy to help, Talk soon! #bobrupakroy
Decision Trees - The Machine Learning Magic Unveiled
Often a Machine Learning algorithm is seen as one of those magical weapons capable of revealing possible future scenarios to whoever holds it. In truth, it's a direct application of mathematical and statistical concepts, which sometimes generate complex models to be interpreted as output. However, there are predictive models based on decision trees that are really simple to understand. In this slide deck I'll explain what is behind a predictive model of this type.
Here the demo files: https://goo.gl/K6dgWC
unit classification.pptx
The document discusses classification techniques for supervised learning problems. It describes classification as predicting categorical class labels based on a training set of labeled data. The classification process involves constructing a model from the training set and then using the model to classify new unlabeled data. Common classification techniques discussed include decision tree induction, Bayesian classification methods, and rule-based classification. Model evaluation and techniques for improving accuracy, such as ensemble methods, are also covered.
Learning
Machine learning is an area of AI concerned with automatic learning. Some ways ML can be used in expert systems include increasing inference efficiency, testing the knowledge base, and acquiring knowledge. The ID3 algorithm constructs a decision tree from a set of examples to derive production rules, aiming to find a small tree efficiently. It selects the attribute with the highest information gain at each node to minimize uncertainty. However, ID3 has limitations such as handling noise, continuous values, and verifying rules on a full dataset.
Decision tree
Decision trees are a non-parametric hierarchical classification technique that can be represented using a configuration of nodes and edges. They are built using a greedy recursive algorithm that recursively splits training records into purer subsets based on splitting metrics like information gain or Gini impurity. Preventing overfitting involves techniques like pre-pruning by setting minimum thresholds or post-pruning by simplifying parts of the fully grown tree. Decision trees have strengths like interpretability but also weaknesses like finding only a local optimum and being prone to overfitting.
Cs501 classification prediction
This document discusses classification and prediction in machine learning. It defines classification as predicting categorical class labels, while prediction models continuous values. The key steps of classification are constructing a model from a training set and using the model to classify new data. Decision trees and rule-based classifiers are described as common classification methods. Attribute selection measures like information gain and gini index are explained for decision tree induction. The document also covers issues in data preparation and model evaluation for classification tasks.
Decision tree
Decision trees are a machine learning technique that use a tree-like model to predict outcomes. They break down a dataset into smaller subsets based on attribute values. Decision trees evaluate attributes like outlook, temperature, humidity, and wind to determine the best predictor. The algorithm calculates information gain to determine which attribute best splits the data into the most homogeneous subsets. It selects the attribute with the highest information gain to place at the root node and then recursively builds the tree by splitting on subsequent attributes.
Data-Mining
The document discusses decision trees for data mining and artificial intelligence. It describes how decision trees are constructed in a top-down manner by choosing attributes that best split the data at each node. The splitting attribute is selected using an impurity measure like information gain or gain ratio, which evaluate how well each attribute separates the data classes. Pruning techniques are also mentioned to simplify trees and avoid overfitting. Examples of decision tree applications in areas like credit risk assessment and disease diagnosis are provided.
unit 5 decision tree2.pptx
The document discusses decision tree construction algorithms. It explains that decision trees are built in a top-down, recursive divide-and-conquer approach by selecting the best attribute to split on at each node, creating branches for each possible attribute value. It also discusses different splitting criteria like information gain and Gini index that are used to determine the best attribute to split on. Finally, it mentions several decision tree algorithms like ID3, C4.5, CART, SLIQ and SPRINT that use these concepts.
Decision tree of cart
Decision trees are a type of predictive model that use a tree-like structure to determine the target variable value based on several input variable values. The document discusses the Classification and Regression Tree (CART) algorithm for generating decision trees. CART uses the Gini index as the splitting criterion to select the most important variables and build the tree by recursively splitting nodes until they are pure or as pure as possible, with one class at each terminal node. An example is provided to demonstrate how CART would build a decision tree to classify families as likely or not likely to purchase a riding lawn mower based on their income and lot size attributes in a sample dataset.
Decision Trees
Decision trees are a supervised learning algorithm that can be used for both classification and regression problems. They work by recursively splitting the data into purer subsets based on feature values, building a tree structure. Information gain is used to determine the optimal feature to split on at each node. Trees are constructed top-down by starting at the root node and finding the best split until reaching leaf nodes. Pruning techniques like pre-pruning and post-pruning can help reduce overfitting. While simple to understand and visualize, trees can be unstable and prone to overfitting.
Decision tree and ensemble
The presentation explains the decision tree and ensemble in machine learning.
I presented this at the Big data club for college students.
(Jan 31st, 2019)
2014-mo444-practical-assignment-04-paulo_faria
The document discusses applying machine learning techniques to identify compiler optimizations that impact program performance. It used classification trees to analyze a dataset containing runtime measurements for 19 programs compiled with different combinations of 45 LLVM optimizations. The trees identified optimizations like SROA and inlining that generally improved performance across programs. Analysis of individual programs found some variations, but also common optimizations like SROA and simplifying the control flow graph. Precision, accuracy, and AUC metrics were used to evaluate the trees' ability to classify optimizations for best runtime.
Similar to ppt on decisions tree descisiontrees-1810518 (1).pptx (20)
Data Mining:Concepts and Techniques, Chapter 8. Classification: Basic Concepts
Data Mining:Concepts and Techniques, Chapter 8. Classification: Basic Concepts
Machine Learning Feature Selection - Random Forest
Machine Learning Feature Selection - Random Forest
Decision Trees - The Machine Learning Magic Unveiled
Decision Trees - The Machine Learning Magic Unveiled
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3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
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3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
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本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
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【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Population Growth in Bataan: The effects of population growth around rural pl...
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ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
一比一原版(Dalhousie毕业证书)达尔豪斯大学毕业证如何办理
原版定制【微信:41543339】【(Dalhousie毕业证书)达尔豪斯大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(BCU毕业证书)伯明翰城市大学毕业证如何办理
原版定制【微信:41543339】【(BCU毕业证书)伯明翰城市大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(Adelaide毕业证书)阿德莱德大学毕业证如何办理
原版定制【微信:41543339】【(Adelaide毕业证书)阿德莱德大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
End-to-end pipeline agility - Berlin Buzzwords 2024
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
一比一原版(UofS毕业证书)萨省大学毕业证如何办理
原版定制【微信:41543339】【(UofS毕业证书)萨省大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Intelligence supported media monitoring in veterinary medicine
Media monitoring in veterinary medicien
一比一原版(CBU毕业证)卡普顿大学毕业证如何办理
CBU毕业证offer【微信95270640】《卡普顿大学毕业证书》《QQ微信95270640》学位证书电子版：在线制作卡普顿大学毕业证成绩单GPA修改（制作CBU毕业证成绩单CBU文凭证书样本）、卡普顿大学毕业证书与成绩单样本图片、《CBU学历证书学位证书》、卡普顿大学毕业证案例毕业证书制作軟體、在线制作加拿大硕士学历证书真实可查.
如果您是以下情况，我们都能竭诚为您解决实际问题：【公司采用定金+余款的付款流程，以最大化保障您的利益，让您放心无忧】
1、在校期间，因各种原因未能顺利毕业，拿不到官方毕业证+微信95270640
2、面对父母的压力，希望尽快拿到卡普顿大学卡普顿大学毕业证成绩单；
3、不清楚流程以及材料该如何准备卡普顿大学卡普顿大学毕业证成绩单；
4、回国时间很长，忘记办理；
5、回国马上就要找工作，办给用人单位看；
6、企事业单位必须要求办理的；
面向美国乔治城大学毕业留学生提供以下服务:
【★卡普顿大学卡普顿大学毕业证成绩单毕业证、成绩单等全套材料，从防伪到印刷，从水印到钢印烫金，与学校100%相同】
【★真实使馆认证（留学人员回国证明），使馆存档可通过大使馆查询确认】
【★真实教育部认证，教育部存档，教育部留服网站可查】
【★真实留信认证，留信网入库存档，可查卡普顿大学卡普顿大学毕业证成绩单】
我们从事工作十余年的有着丰富经验的业务顾问，熟悉海外各国大学的学制及教育体系，并且以挂科生解决毕业材料不全问题为基础，为客户量身定制1对1方案，未能毕业的回国留学生成功搭建回国顺利发展所需的桥梁。我们一直努力以高品质的教育为起点，以诚信、专业、高效、创新作为一切的行动宗旨，始终把“诚信为主、质量为本、客户第一”作为我们全部工作的出发点和归宿点。同时为海内外留学生提供大学毕业证购买、补办成绩单及各类分数修改等服务；归国认证方面，提供《留信网入库》申请、《国外学历学位认证》申请以及真实学籍办理等服务，帮助众多莘莘学子实现了一个又一个梦想。
专业服务，请勿犹豫联系我
如果您真实毕业回国，对于学历认证无从下手，请联系我，我们免费帮您递交
诚招代理：本公司诚聘当地代理人员，如果你有业余时间，或者你有同学朋友需要，有兴趣就请联系我
你赢我赢，共创双赢
你做代理，可以帮助卡普顿大学同学朋友
你做代理，可以拯救卡普顿大学失足青年
你做代理，可以挽救卡普顿大学一个个人才
你做代理，你将是别人人生卡普顿大学的转折点
你做代理，可以改变自己，改变他人，给他人和自己一个机会道银边山娃摸索着扯了扯灯绳小屋顿时一片刺眼的亮瞅瞅床头的诺基亚山娃苦笑着摇了摇头连他自己都感到奇怪居然又睡到上午点半掐指算算随父亲进城已一个多星期了山娃几乎天天起得这么迟在乡下老家暑假五点多山娃就醒来在爷爷奶奶嘁嘁喳喳的忙碌声中一骨碌爬起把牛驱到后龙山再从莲塘里采回一蛇皮袋湿漉漉的莲蓬也才点多点半早就吃过早餐玩耍去了山娃的家在闽西山区依山傍水山清水秀门前潺潺流淌的蜿蜒小溪一直都是山娃和小伙伴们盛试
一比一原版(Harvard毕业证书)哈佛大学毕业证如何办理
毕业原版【微信:41543339】【(Harvard毕业证书)哈佛大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理
毕业原版【微信:176555708】【(UCSB毕业证书)圣芭芭拉分校毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
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ppt on decisions tree descisiontrees-1810518 (1).pptx
- 2. Introduction Decision trees Decision trees are a model where we break our data by making decisions using series of conditions(questions).
- 3. Decision tree algorithm ● These are also termed as CART algorithms. ● These are used for ○ Classification and ○ Regression ● Classification and Regression Trees
- 4. Decision tree components ● Root node ○ It refers to the start of the decision tree with maximum split ( information Gain) ● Node ○ Node is a condition with multiple outcomes in the tree. ● Leaf ○ This is the final decision(end point) of a node from the condition(question)
- 6. Every node yields maximum data in each split which could be achieved by IG Information Gain ( IG )
- 7. It can be calculated by using impurity measures of each split 1. Gini Index (Ig) 2. Entropy ( Ih ) 3. Classification error ( Ie ) Impurity Metrics
- 8. ● Root node is split to get maximum info gain. ● Increase in nodes in the tree causes overfitting. ● Splitting continues until each of the leaf is pure ( one of the possible outcome ) ● Pruning can also be done which means removal of branches which use features of low importance. ● Gini index ≅Entropy ● If uniform distribution , entropy is 1 Principle of spliting nodes
- 9. Split A Parent data set ---> 40 items in feature 1 and 40 items in feature 2 Child 1 → 30 items in feature 1 and 10 items in feature 2 Child 2 → 10 items in feature 1 and 30 items in feature 2 Split B Parent data set ---> 40 items in feature 1 and 40 items in feature 2 Child 1 → 20 items in feature 1 and 40 items in feature 2 Child 2 → 20 items in feature 1 and 0 items in feature 2
- 13. Comparison of all Impurity Metrics Scaled Entropy = Entropy /2 Gini index is intermediate values of impurity lying between classification error and Entropy .
- 14. Pros : ● Simple to understand, interpret, visualize. ● It is effective to use in numerical and categorical data outcomes. ● Requires little effort from users for data preparation. ● Nonlinear relationships between parameters do not affect tree performance. ● Able to handle irrelevant attributes ( Gain = 0 )
- 15. Cons : ● May make a complex tree with maximum depth. ● Unstable as small variation in input data may result in completely different tree to get generated. ● As it is a greedy algorithm , may not find globally best tree for a data set .
- 16. Applications : 1. Business Management 2. Customer Relationship Management 3. Fraudulent Statement Detection 4. Engineering 5. Energy Consumption 6. Fault Diagnosis 7. Healthcare Management
- 17. References : ● Python Machine Learning By Sebastian Raschka ● https://towardsdatascience.com/decision-trees-in-machine-learning-641b9c4e8 052 ● https://www.bogotobogo.com/python/scikit-learn/scikt_machine_learning_Decis ion_Tree_Learning_Informatioin_Gain_IG_Impurity_Entropy_Gini_Classificatio n_Error.php ● https://media.ed.ac.uk/media/Pros+and+cons+of+decision+trees/1_p4gyge5m ● https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4466856/ ● http://what-when-how.com/artificial-intelligence/decision-tree-applications-for-d ata-modelling-artificial-intelligence/
- 18. Let’s code now Data used : Iris from Sklearn Plots : Matplotlib Splits : Two features at a time File : dtree.py Link to code : Click here for code
- 19. Thank You