机器学习

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Exercises

浏览 149 扫码 分享 2023-11-22 01:00:35

    Exercise: Model Selection using t-test
    Solution to Exercise: Model Selection using t-test

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    • 书签
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    • 8715
    • LeetCode
    • Linux
    • 机器学习
      • 数据清洗
        • Lec 1-6
        • Lec 7-14
        • 12. Record linkage
        • 13-14. Pre-proccessing & Blocking
        • Lec 15 - 16 Record pair comparison
        • Lec 17-18 Record pair classification
        • 19. Record linkage evaluation
        • 20. Clerical review
        • 21. Data Fusion
        • 22. Advanced record linkage techniques
        • 23. Privacy aspects in data wrangling and privacy-preserving record linkage
        • 24. Ontology matching 本体匹配
        • 25. Wrangling dynamic and spatial data
      • 文档分析｜自然语言处理
        • IR
          • Information retrieval (IR) L1
          • Information retrieval (IR) L2
          • Information retrieval (IR) L3
          • Web search
        • Mechine learning
          • L1 Linear Regression
          • L2 Representation in NLP
          • L3 Deep Neural Networks
          • L4 Deep Neural Networks in Practice
          • L5 Deep Neural Networks for Structured Data
          • L6 DNN – Attention
          • L7 DNN-Transformers
          • L8 Pre-training and Neural Language Models
          • L9 Clustering
        • NLP
          • L1 Semantics
          • L2 Constituency Parsing 成分句法分析
          • L3 Dependency Parsing 依赖解析
          • L4 Language Models
      • 数据挖掘
        • 问题汇总
          • quiz
        • 分类和预测（Classification & Prediction）
          • Two steps: Construct and Evaluate
          • Evaluation
          • Decision Trees 决策树
            • Basic, greedy, decision tree algorithm
            • Attribute Selection Methods
              • Information Gain
              • Information Gain for continuous-valued attributes
              • Gain Ratio
              • Gini index
              • Other Attribute Selection Methods
            • Overfitting and tree pruning
            • Enhancements to the Basic Algorithm
            • Extracting Rules from Decision Trees
          • Rule learning 规则学习
            • Rule-Based Classification
            • Rule Induction
          • Bayes Classifiers 贝叶斯分类器
            • What is Bayesian classifier?
              • Basic Probabilities
              • Bayes' Theorem
              • Limitation
            • Naive Bayes 朴素贝叶斯
              • Laplacian Correction
              • Numerical attributes
            • Bayesian Belief Networks
              • Training a Belief Network
          • Evaluation of Classifiers
            • Model Evaluation and Selection
              • Evaluation metrics for classification
              • Estimating a classifier’s accuracy
              • ROC Curve
              • Comparing classifiers
              • Exercises
              • Other issues affecting the quality of a model
          • Lazy Learners
            • Case-Based Reasoning (CBR)
            • K-Nearest Neighbourhood (k-NN)
          • SVM(Support vector machine)支持向量机
            • Linearly Separable Case 线性可分
            • Linearly Inseparable Case 线性不可分情况
          • Neural Nets 神经网络
            • Multi-Layer Feed-Forward Neural Network
            • Prediction with Neural Network
            • Training a Neural Network
            • Performance Evaluation
            • Other Neural Net Architectures
          • Linear Regression 线性回归
            • Simple Linear Regression
            • Multiple Linear Regression
          • Variants of Classification
          • Other "Soft" Classification Algorithms
        • Cluster Analysis 聚类分析
          • Clustering: Basic Concepts
            • Quality of Clustering
            • Considerations
            • Major Approaches
          • Partitioning Methods (K-means)
            • Strength and Weakness
            • K-Medoids (PAM)
            • Exercise: K-means clustering
          • Hierarchical Clustering (AGNES and DIANA)
            • Distance between Clusters
            • Dendrogram 系统树图
          • Density-Based Clustering
          • Grid-Based Approach
          • Evaluation of Clustering
            • Assessing Clustering Tendency
            • Determine the Number of Clusters, k
            • Measure Clustering Quality
        • Outlier Detection
          • What are Outliers?
          • Statistical Approaches
            • Parametric Methods: Univariate Outliers from a Normal Distribution
            • Parametric Methods: Multivariate Outliers
            • Parametric Methods: Mixture of Parametric Distributions
          • Proximity-Based Approaches 基于邻近的方法
            • Distance-Based Outlier Detection: Nested loop method
              • Example: Distance-Based Outlier with Nested Loop
            • Density-based Outlier Detection
          • Clustering Based Approaches 基于聚类的方法
          • Classification-Based Approaches
          • Contextual and Collective Outliers
        • Specialist topics
          • Data Stream Mining 数据流挖掘
            • Stream OLAP
            • Synopsis Methods for Stream Data Processing 流数据处理的简要方法
            • Frequent Pattern Mining
            • Clustering
          • Ensemble Methods
            • Bagging
            • Boosting with AdaBoost
              • Example: AdaBoost ensemble method
          • Time Series Analysis 时间序列分析
            • Trend Analysis
            • Similarity Search 相似性搜索
        • Text and Web Mining
          • Basic Measures for Information Retrieval
          • Web mining
            • Mining Page Structure
            • Mining Web link structure
            • Mining Multimedia on the Web
            • Web Usage Mining
          • Word meaning by embedding
          • Text mining problems
            • Keyword based association analysis
            • Text Classification
            • Document Clustering
          • Text Data Analysis and Information Retrieval
            • Information Retrieval Techniques
            • Selecting index terms
            • building an index
            • Matching the query to the index
            • How to assign weights to term occurrences
            • Putting it together: Ranking in the vector space model
        • Semantic Web and Knowledge GraphsBook
          • Semantic Web Mining 语义网挖掘
            • OWL learning: Example
            • Refinement-based Classification
            • Summary
          • introduction
          • Foundation technologies
            • Universal Resource Identifiers
            • URI structure
            • RDF/RDFs
            • RDF for relations
            • How to encode Literals that are not things
            • Namespace abbreviations
            • A graph is a set of Triples
            • Typed Literals
            • Things can also be typed
            • SPARQL: Querying RDF
          • The Web Ontology Language 网络本体语言(OWL)
            • OWL preliminaries
            • OWL Classes
            • Relating Classes and Properties
            • OWL individuals
          • Knowledge Graphs
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