Problem statementSend feedbackYou have been provided with a customer dataset:Why do you think there is a need to do feature scaling in this dataset?
Question
Problem statementSend feedbackYou have been provided with a customer dataset:Why do you think there is a need to do feature scaling in this dataset?
Solution
Feature scaling is a method used to normalize the range of independent variables or features of data. In data processing, it is also known as data normalization and is generally performed during the data preprocessing step.
The necessity of feature scaling in a dataset can be justified with the following points:
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Different Measurement Scales: Features in the dataset can be measured in different units, such as age (in years), income (in dollars), and height (in cm or inches). These features have different scales and might have a large difference in their ranges. Algorithms that use Euclidean Distance measure are sensitive to these scales and can underperform if scales are not normalized.
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Speeds up Convergence: Many machine learning algorithms use gradient descent as an optimization technique. Feature scaling can speed up the convergence of these algorithms.
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Prevents Overfitting: Feature scaling helps to prevent overfitting by ensuring that all features contribute equally to the model.
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Improves Model Performance: Some machine learning algorithms, like support vector machines (SVM) and k-nearest neighbors (KNN), perform better on scaled data.
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Helps in Visualization: Feature scaling helps in data visualization as features will have the same scale on the graph.
Therefore, considering the above points, there is a need to do feature scaling in the provided customer dataset.
Similar Questions
What is the purpose of feature scaling in machine learning?Question 10Answera.To remove outliers from the datab.To standardize the range of featuresc.To increase the complexity of modelsd.To decrease the dimensionality of features
hich of the following(s) is/are feature scaling techniques?
What is NOT a benefit of data scaling? Group of answer choicesData scaling reduces the chance of mistakes.Data scaling can make the visualisation of the data more sensible and tidy.Data scaling can help to make further calculations more efficient.Scaling can help to mitigate the effect of outliers in the data.If your features have different units or scales, it can be challenging to interpret the overall effect of each feature on an outcome variable. Data scaling helps with this.
Question 4Which of the following statements about scaling features prior to regularization is TRUE?1 pointFeature scaling is not recommented prior to regularization.Features should rarely or never be scaled prior to implementing regularization.The larger a feature’s scale, the more likely its estimated impact will be influenced by regularization.The smaller a feature’s scale, the more likely its estimated impact will be influenced by regularization.
Do all features need to be scaled when using machine learning algorithms?
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