From 910e735e986cd2e4e1260aa6eec0c039bed38ee0 Mon Sep 17 00:00:00 2001 From: Anirban Mukherjee Date: Sat, 9 Oct 2021 02:45:52 +0530 Subject: [PATCH] Added questions and answers for quiz on SVR --- 7-TimeSeries/3-SVR/README.md | 5 +- quiz-app/src/assets/translations/en.json | 114 +++++++++++++++++++++++ 2 files changed, 118 insertions(+), 1 deletion(-) diff --git a/7-TimeSeries/3-SVR/README.md b/7-TimeSeries/3-SVR/README.md index da50702f..0b7d8336 100644 --- a/7-TimeSeries/3-SVR/README.md +++ b/7-TimeSeries/3-SVR/README.md @@ -2,6 +2,7 @@ In the previous lesson, you learned how to use ARIMA model to make time series predictions. Now you'll be looking at Support Vector Regressor model which is a regressor model used to predict continuous data. +## [Pre-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/51/) ## Introduction @@ -12,7 +13,7 @@ In this lesson, you will discover a specific way to build models with [**SVM**: Before understanding the importance of SVR in time series prediction, here are some of the important concepts that you need to know: - **Regression:** Supervised learning technique to predict continuous values from a given set of inputs. The idea is to fit a curve (or line) in the feature space that has the maximum number of data points. [Click here](https://en.wikipedia.org/wiki/Regression_analysis) for more information. -- **Support Vector Machine (SVM):** A type of supervised machine learning model used for classification, regression and outliers detection. The model is a hyperplane in the feature space, which in case of classification acts as a boundary, and in case of regression acts as the best-fit line. In SVM, a Kernel function is generally used to transform the dataset, so that a non-linear decision surface is able to transform to a linear equation in a higher number of dimension spaces. [Click here](https://en.wikipedia.org/wiki/Support-vector_machine) for more information. +- **Support Vector Machine (SVM):** A type of supervised machine learning model used for classification, regression and outliers detection. The model is a hyperplane in the feature space, which in case of classification acts as a boundary, and in case of regression acts as the best-fit line. In SVM, a Kernel function is generally used to transform the dataset to a space of higher number of dimensions, so that they can be easily separable. [Click here](https://en.wikipedia.org/wiki/Support-vector_machine) for more information on SVMs. - **Support Vector Regressor (SVR):** A type of SVM, to find the best fit line (which in the case of SVM is a hyperplane) that has the maximum number of data points. ### Why SVR? [^1] @@ -361,6 +362,8 @@ MAPE: 2.0572089029888656 % - Try to use different kernel functions for the model and analyze their performances on the dataset. A helpful document can be found [here](https://scikit-learn.org/stable/modules/svm.html#kernel-functions). - Try using different values for `timesteps` for the model to look back to make prediction. +## [Post-lecture quiz](https://white-water-09ec41f0f.azurestaticapps.net/quiz/52/) + ## Review & Self Study This lesson was to introduce the application of SVR for Time Series Forecasting. To read more about SVR, you can refer to [this blog](https://www.analyticsvidhya.com/blog/2020/03/support-vector-regression-tutorial-for-machine-learning/). This [documentation on scikit-learn](https://scikit-learn.org/stable/modules/svm.html) provides a more comprehensive explanation about SVMs in general, [SVRs](https://scikit-learn.org/stable/modules/svm.html#regression) and also other implementation details such as the different [kernel functions](https://scikit-learn.org/stable/modules/svm.html#kernel-functions) that can be used, and their parameters. diff --git a/quiz-app/src/assets/translations/en.json b/quiz-app/src/assets/translations/en.json index 337b0867..640c90f0 100644 --- a/quiz-app/src/assets/translations/en.json +++ b/quiz-app/src/assets/translations/en.json @@ -2809,6 +2809,120 @@ ] } ] + }, + { + "id": 51, + "title": "Time Series SVR: Pre-Lecture Quiz", + "quiz": [ + { + "questionText": "SVM stands for", + "answerOptions": [ + { + "answerText": "Statistical Vector Machine", + "isCorrect": "false" + }, + { + "answerText": "Support Vector Machine", + "isCorrect": "true" + }, + { + "answerText": "Statistical Vector Model", + "isCorrect": "false" + } + ] + }, + { + "questionText": "Which of these ML techniques is used to predict continuous values?", + "answerOptions": [ + { + "answerText": "Clustering", + "isCorrect": "false" + }, + { + "answerText": "Classification", + "isCorrect": "false" + }, + { + "answerText": "Regression", + "isCorrect": "true" + } + ] + }, + { + "questionText": "Which of these models is popularly used for time series forecasting?", + "answerOptions": [ + { + "answerText": "ARIMA", + "isCorrect": "true" + }, + { + "answerText": "K-Means Clustering", + "isCorrect": "false" + }, + { + "answerText": "Logistic Regression", + "isCorrect": "false" + } + ] + } + ] + }, + { + "id": 52, + "title": "Time Series SVR: Post-Lecture Quiz", + "quiz": [ + { + "questionText": "By which of these methods does an SVR learn?", + "answerOptions": [ + { + "answerText": "Finding the best fit hyperplane that has the maximum number of data points", + "isCorrect": "true" + }, + { + "answerText": "Learning the probability distribution of the dataset", + "isCorrect": "false" + }, + { + "answerText": "Finding clusters in the dataset", + "isCorrect": "false" + } + ] + }, + { + "questionText": "What is the purpose of a kernel in SVMs?", + "answerOptions": [ + { + "answerText": "To measure the accuracy of the model predictions", + "isCorrect": "false" + }, + { + "answerText": "To transform the dataset to a higher dimension space", + "isCorrect": "true" + }, + { + "answerText": "To standardize the values of the dataset", + "isCorrect": "false" + } + ] + }, + { + "questionText": "Which of these models consider the non-linearity in the dataset?", + "answerOptions": [ + { + "answerText": "Simple Linear Regression", + "isCorrect": "false" + }, + { + "answerText": "ARIMA", + "isCorrect": "false" + }, + { + "answerText": "SVR using RBF kernel", + "isCorrect": "true" + } + ] + } + ] } ] }