Ignoring Feature Scaling
In data science, Feature scaling is a preprocessing technique that transforms numerical variables measured in different units into a common unit. This facilitates robust and efficient model training. Feature scaling helps modify the magnitude of individual features and does not influence the behavior of the machine learning algorithm. Algorithms like gradient descent converge faster when numbers are on a similar scale. In the world of data, variables are the features that take different units. Scaling will adjust all different units into a single unit to make sure no single feature overpowers others just because of measuring units.
Causes of Ignoring Feature Scaling
Example: Assumption of similar scale Let’s consider a dataset with age and income variables. Age is in the range of 20 to 60 and income is in the range 10000 to 100000. If both the features are treated equally, the model will be biased towards income. So it’s essential to convert both the features to a similar scale to get accurate predictions.
Key Aspects of Feature Scaling
- Min-Max scaling: It is a method of normalizing input features/variables. By using min-Max scaler all features are transformed to a range of 0 to 1. It means the minimum variable will be mapped to 0 and the maximum to 1.
- Standardization: In this method, the values are centered around the mean with the unit standard deviation. It means that if the mean of an attribute becomes zero, it means the resultant distribution has a unit standard deviation.
- Robust scaling: In some situations, the extreme values negatively impact other features of the data set. To overcome this, robust scaling uses median and Interquartile Range(IQR). It transforms all the data points within the range of median to IQR value.
Practical Tips
- By applying feature scaling consistently across numerical variables to ensure uniformity.
- Experiment with Min-Max scaling, standardization, and robust scaling to understand their impact on model performance.
6 Common Mistakes to Avoid in Data Science Code
As we know Data Science is a powerful field that extracts meaningful insights from vast data. It is our job to discover hidden secrets from the available data. Well, that is what data science is. In this world, we use computers to solve problems and bring out hidden insights. When we enter into such a big journey, there are certain things we should watch out for. Those who like playing with data know the tricky part of understanding the data and the possibility of making mistakes during the data processing.
How can I avoid mistakes in my Data Science Code?
How can I write my Data Science code more efficiently?
To answer all your questions, In this article, you get to know Six common mistakes to avoid in data science code in detail.
Table of Content
- Ignoring Data Cleaning
- Neglecting Exploratory Data Analysis
- Ignoring Feature Scaling
- Using default Hyperparameters
- Overfitting the Model
- Not documenting the code
- Conclusion
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