Regression
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Regression
- All Machine Learning resources
- Types of Learning26
- Machine Learning Workflow and Deployment13
- Data Preparation7
- Feature Engineering and Dimensionality Reduction12
- Model Training and Optimization6
- Model Evaluation and Generalization14
- Classification8
- Decision Trees and Ensemble Learning11
- Support Vector Machines and Kernel Methods5
- Bayesian and Probabilistic Models13
- Clustering18
- Neural Networks and Deep Learning5
- Association Rules and Recommender Systems5
- Machine Learning Applications10
Top Regression resources
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7 questionsLogistic regression Logistic regression is a classification algorithm used to assign observations to a discrete set of classes.
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74 cardsITEC 620 - FINAL
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13 questionsLinear Regression Regression analysis is a form of predictive modelling technique which investigates the relationship between...
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22 questionsRegression Analysis Test - Machine Learning All explanatory variables are significant at 90% confidence level
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17 cardsMultiple linear regression What can a “multiple linear regression” be used for?
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16 questionsModule 5 Review What is typically the best type of model?
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19 cardsex2 ml lectNote multivariable hypothesis function
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17 questionsLogit Regression Test - Machine Learning Logit uses a reference line as relation between dependent & independents
Quizzes
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7 questionsLogistic regression Logistic regression is a classification algorithm used to assign observations to a discrete set of classes.
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13 questionsLinear Regression Regression analysis is a form of predictive modelling technique which investigates the relationship between...
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22 questionsRegression Analysis Test - Machine Learning All explanatory variables are significant at 90% confidence level
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16 questionsModule 5 Review What is typically the best type of model?
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17 questionsLogit Regression Test - Machine Learning Logit uses a reference line as relation between dependent & independents
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6 questionsLinear Regression KHT - Machine Learning Which of these statements regarding Linear Regression is CORRECT?
Mind maps
Flashcards
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74 cardsITEC 620 - FINAL
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17 cardsMultiple linear regression What can a “multiple linear regression” be used for?
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19 cardsex2 ml lectNote multivariable hypothesis function
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6 cardsLinear and Dual Regression Linear regression modelling guidelines?
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1 cardsUntitled Our objective is to get the best possible line. The best possible line will be such so that the average squ...
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1 cardsRegression What is regression?
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2 cardsUntitled Logistic Function or Sigmoid Function
Notes
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week 3
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Machine Learning Types of Machine Learning Unsupervised ML Only inputs are provided, and the machine groups the data of its own. Eg: grouping news article of same type, grouping customers into different market segments. Grouping can b...
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Supervised Learning Regression Analysis : Terminologies : 1. Target Variable - what we need to get also called Dependent variable 2. Independent Variables : this are the factors affecting the target variables also...
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Untitled Linear Regression Our goal in linear regression is to predict a target value yy starting from a vector of input values x∈Rnx∈ℜn. For example, we might want to make predictions about the price of ...
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multivariate regression single variate regression: h-theta = theta-0 + theta-1 * x multi-variate: h-theta = theta-0 + theta-1 * x1 + theta-2 * x2 + theta-3*x3 +.....theta-n * xn alternately: h-theta = theta-0 * x-0 + theta-1 * x1 + the...
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polynomial regression
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Lecture1 notes If we want to penalise the formula why to use the absolute value rather than the square? L1 constraint is another name for the Lasso, while l2 might describe the ridge regression Ridge is lik...
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English Sentence Hi folks, last time I wrote about Classification and Regression, by now I expect you to be able to differentiate regression and classification problem. Today we are going to define a regression problem and apply ...
Slides
More Machine Learning topics
All of Machine LearningTypes of LearningMachine Learning Workflow and DeploymentData PreparationFeature Engineering and Dimensionality ReductionModel Training and OptimizationModel Evaluation and GeneralizationClassificationDecision Trees and Ensemble LearningSupport Vector Machines and Kernel MethodsBayesian and Probabilistic ModelsClusteringNeural Networks and Deep LearningAssociation Rules and Recommender SystemsMachine Learning Applications