Exam Professional Machine Learning Engineer topic 1 question 115 discussion

D. Normalize the data by scaling it to have values between 0 and 1 (Min-Max scaling): This technique ensures all features contribute proportionally to the model's learning process. pen_spark expand_more It prevents features with a larger magnitude from dominating the model and reduces the risk of overfitting.expand_more

agree with pico

Not A because a logarithmic transformation may be appropriate for data with a skewed distribution, but it doesn't necessarily address the issue of features having different scales.

Features with a larger magnitude might still dominate after a log transformation if the range of values is significantly different from other features. Scaling is better, will go with Option D

by abylead: Min-Max scaling is a popular technique for normalizing stock price data. Logs are commonly used in finance to normalize relative data, such as returns.https://itadviser.dev/stock-market-data-normalization-for-time-series/

D is the right answer

go for D, z-score. This question doesn't mention outlier, just large range. reason why not log transformation: log transformation is more suitable for addressing skewed distributions and reducing the impact of outliers. It compresses the range of values, especially for features with a large dynamic range. While it can help normalize the distribution, it doesn't directly address the issue of feature magnitude overpowering the model.

From my point of view, log transformation is more tolerant to outliers. Thus, went to A.

See https://developers.google.com/machine-learning/data-prep/transform/normalization

A is a better option because Log transform data used when we want a heavily skewed feature to be transformed into a normal distribution as close as possible, because when you normalize data using Minimum Maximum scaler, It doesn't work well with many outliers and its prone to unexpected behaviours if values go out of the given range in the test set. It is a less popular alternative to scaling.

The challenge is the “scale” (significant variations in magnitude and spread): https://stats.stackexchange.com/questions/462380/does-data-normalization-reduce-over-fitting-when-training-a-model, apparently largely used anyhow: https://itadviser.dev/stock-market-data-normalization-for-time-series/.

The question doesn't talk about the skewness within each feature. It talks about normalizing the effect of features with large range. So scaling each feature within (0,1) range will solve the problem

Really need more info to answer this: what does "large range" mean? Distribution follows a power law --> use log(). Or are they more evenly/linearly distributed --> use (0,1) scaling.

I think C could be a better choice. Bucketizing the data we can fix the distribution problem by bins. in letter A, standardization by log could not be effective if the range of the data has negative and positive values. In letter D, definitely normalization does not resolve the skew problem. Data normalization assumes that data has some normal distribution. https://medium.com/analytics-vidhya/data-transformation-for-numeric-features-fb16757382c0

D. Normalize the data by scaling it to have values between 0 and 1. Standardization and normalization are common techniques to preprocess the data to be more suitable for machine learning models. Normalization scales the data to be within a specific range (commonly between 0 and 1 or -1 and 1), which can help prevent features with large magnitudes from dominating the model. This approach is especially useful when using models that are sensitive to the magnitude of features, such as distance-based models or neural networks.

https://developers.google.com/machine-learning/data-prep/transform/normalization#log-scaling

he best approach to handle features with a large range in an ML model is to normalize the data by scaling it to have values between 0 and 1. Therefore, the correct answer is D. Normalization ensures that the features have similar scales, which is important for many machine learning algorithms. If some features have a larger magnitude than others, they can dominate the objective function and make the model unable to learn from other features correctly. By scaling all the features to a similar range, we can avoid this problem and make the objective function less sensitive to the scale of the input features. Standardizing the data by transforming it with a logarithmic function (option A) is not suitable for all types of data and may not always be effective in reducing the impact of features with a large range.