Data Preprocessing Techniques for Machine Learning

Data Preprocessing Techniques for Machine Learning Guide

One of the most challenging and time-consuming parts of data science is related to the data pre-processing stage, but despite its challenges, this part is considered one of the most important parts. The model can be harmed if the data cannot be processed and cleansed. Scientists are aware that to improve the usability of real-world data, they must apply certain Data Preprocessing Techniques for Machine Learning. These techniques make it easy to use in machine learning algorithms and lead to a better model.

In addition to this introduction, we intend to provide an overview of data preprocessing and the reason for its importance, as well as the main techniques used in this important stage of data science.

What is Data Preprocessing?

The Importance of Data Preprocessing

If the data pre-processing step is not considered, it will have an impact on the way forward and when applying the data set to the machine learning model. Most models cannot handle missing values, and some of them are affected by outliers, high dimensions, and noisy data; As a result, with the help of the data preprocessing stage, the data set becomes more complete and accurate. This step is very important to make the necessary adjustments in the data before entering the data set into the machine learning model.

What are the Important Data Preprocessing Techniques for Machine Learning?

Now that we know the importance of the data pretreatment step, let’s look at the main techniques for handling the data and see how we can apply them to future projects. Data Preprocessing Techniques for Machine Learning include the following:

Data cleanup

Identifying and correcting bad and incorrect observations from the data set is one of the most important aspects of the data preprocessing stage, with the help of this work, the quality is improved. This method is used to find missing, inaccurate, duplicate, unnecessary, or null values in the data. After identifying these problems, they should be corrected or eliminated. In this situation, the strategy you use is related to the problem area and the goal of your project. Let’s discuss some common problems we deal with when analyzing data and how to solve them:

Noisy data

Usually, these data are meaningless in the dataset that contains false records or duplicate observations. For example, we could point out that a database has a negative age value. This case is not logical, so you can delete it or consider it as zero.

Solution: There is a common technique for noisy data called binning in which the data values are sorted first and then you divide them into bins and then apply a mean/median in each bin, smoothing it.

Noisy data

Structural errors

Typographical errors and inconsistent data values are commonly referred to as structural errors. Manual methods and solutions ought to be applied in this situation. Inconsistencies and typos can also be fixed in a variety of scenarios with simple or complex changes.

Missing data

Missing data points is another common problem. Most machine learning models cannot handle missing values in the data, so you must adjust the data so that it is properly used within the model. The methods to deal with this problem include the following:

The first solution: The simplest solution is to delete these observations, although this solution is recommended in the following situations:

  1. Your data set is large and you have some missing records, so removing them does not affect the distribution of the data set.
  2. The observation itself is meaningless because the majority of its characteristics are false.

Second solution: using the backward/forward fill method, where you select the previous or next value to fill the missing value.

Dimension reduction

Dimensionality reduction is related to reducing the number of input features in the training data. With a real-world data set, there are usually a large number of features, and if this number of features is reduced, it may affect the model’s performance later when we feed this data set to it. Reducing the number of features while maintaining a large amount of diversity in the dataset has many benefits, including:

  • Need fewer computing resources
  • Increase the overall performance of the model
  • Avoiding overfitting (when the model becomes too complicated, instead of learning, it keeps the training data, as a result, the performance in the test data is greatly reduced)
  • By avoiding multicollinearity—a high correlation between one or more independent variables—this method also lessens noise in the data.

In this section, we want to discuss the main types of dimensionality reduction that we can apply to data:

Feature selection

Feature selection refers to the process of selecting the most important features of your predictor variable, that is, selecting the features that contribute the most to your model. In this section, there are techniques you can apply to this approach either automatically or manually:

  • Correlation between features: This approach is very common and removes some features that are highly correlated with others.
  • Statistical tests: Another solution is to use statistical tests to select features that investigate the relationship of each feature individually with the output variable.
  • Recursive Feature Elimination (RFE): In this solution, the algorithm trains the model with all the features in the data set, calculates the model’s performance, and then removes one feature at a time when the performance improvement is small.
  • Variance Threshold: In this solution, features with high variation in the column are identified and those that cross the threshold are selected. This solution is predicated on the idea that characteristics that exhibit minimal variability within themselves have minimal impact on the output variable. Also, some models automatically apply a feature selection during training. Decision tree-based models can rate each feature in the data and provide information about the significance of each feature. It is more pertinent to the model the higher this value is.

Linear methods

When using linear methods, the dimensionality of the data is reduced through a linear transformation. The most common approach is principal component analysis, a method that transforms the principal features into another dimensional space, capturing many of the original data changes with a much smaller number. The original data’s interpretability is lost in the newly modified characteristics, and it is limited to working with quantitative variables.

Feature engineering: using domain knowledge to create features

The feature engineering approach is used to create better features for the data set that increases the model’s performance. We usually use domain knowledge to create features by manually creating existing features by making changes to them.

Feature engineering: using domain knowledge to create features

Managing large amounts of data (sampling data)

Although the more data there is, the more accurate the model is, some machine learning algorithms may have difficulty managing a large amount of data, with problems such as memory saturation, increased computation to adjust model parameters, and other issues facing each other.

To solve this problem, here are some data sampling techniques that we can use:

  • Sampling without replacement: This approach avoids repeating the same data in the sample, so if the record is selected, it is removed from the population.
  • Sampling with replacement: With this approach, the object is not removed from the population and can be repeated multiple times for the sample data because it can be sampled more than once.
  • Stratified sampling: This method is more complicated and divides the data into many partitions. It refers to taking random samples for each partition. In cases where the classes are disproportionate, this approach maintains a proportional number of classes concerning the original data.
  • Progressive sampling: This technique starts with a small size and increases the data set until you have a sufficient sample size.

Data conversion: Converting data into a structure

One of the most important parts of the preprocessing stage is data conversion, which converts data from one format to another. Some algorithms expect the input data to be transformed, so if this process is not completed, the model may perform poorly.

Some of the main techniques used to deal with this problem are:

Transformation for categorical variables

Classification variables, which are usually expressed through text, are not directly used in most machine learning models, so it is necessary to obtain numerical encodings for classification features. The approach used depends on the type of variables.

Min-Max Scaler / Normalization

Min-Max Scaler, also known as normalization, is one of the most common scalers and refers to scaling data between a predefined range. The main problem with this technique is that it is sensitive to outliers, but it is worth using when the data does not follow a normal distribution.

Standard scaler

Another popular method for z-score normalization or standardization is the standard scaler. By transforming the data, it makes the standard deviation equal to one and the mean equal to zero. This approach works best with data that follows a normal distribution and is not sensitive to outliers.

Data conversion: Converting data into a structure

Data management with uneven distribution of classes (unbalanced data)

One of the most common problems encountered when dealing with real-world data classification is that the classes are unbalanced (for example, one class has more samples than the other), which creates a strong bias for the model.

There are three main techniques you can use to address this deficiency in your dataset:

Oversampling

This technique is the process of augmenting the dataset with minority-class artificial data. The most popular technique used for this method is artificial minority oversampling.

Undersampling

This technique is the process of reducing the data and removing the real data from the majority class. The main algorithms used in this approach are TomekLinks and Edited Nearest Neighbors (ENN).

Hybrid

This strategy combines the first two methods mentioned above. SMOTEENN, which employs the SMOTE algorithm for oversampling in the minority class and the ENN algorithm for undersampling in the majority class, is one of the algorithms used in this technique.

Last words

The data preprocessing step is very important to specify the correct input data for machine learning algorithms. Without using proper techniques, the result of the model will be spoiled. Keep in mind that you should divide your data set into training and testing sets before using these techniques and only use the training set to learn and use it in the test.

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