Data mining is the process of looking for hidden relationships, patterns, and anomalies in huge datasets to estimate results. Many variables in large datasets are growing exponentially. As a result, the data mining process requires a lot of resources and computer time to unidimensionality reduction techniques are a part of the pre-processing data stage that is carried out before training the model.
Machine learning involves a lot of computations and resources, not to mention the manual effort that goes along with it, to analyze data using a list of variables. The dimensionality reduction approaches are very useful in this situation. A high-dimensional dataset can be converted into a lower-dimensional dataset using the dimensionality reduction technique without sacrificing any of the important characteristics of the original data. These dimensionality reduction methods fall under the category of data pre-processing, which is done before model training.
What is Dimensionality Reduction related to Data Science?
Consider developing a model that can forecast the weather for the following day using the current climatic circumstances. Millions of such environmental features are too difficult to examine, including sunlight, humidity, cold, temperature, and many others that could influence the current conditions. Therefore, by identifying the features with a high degree of correlation and grouping them together, we may reduce the number of features.
Since we know their significant correlation, we can combine humidity and rainfall into a single dependent characteristic in this instance. I'm done now! By using the dimensionality reduction technique, complex data can be condensed into a more manageable format without losing its original meaning. Data science and AI specialists also leverage business ROI using data science solutions.
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over and analyze patterns. As a result, the dimensionality reduction technique can be used during data mining to restrict those data features by grouping them while adequately representing the original information.
Benefits of DR
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Data visualization gets simpler
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The dependent variables are not multicollinear.
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Decreased likelihood of overfitting the model
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Less processing time and storage space are required.
Drawbacks of DR
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PCA cannot be used when data cannot be characterized using mean and covariance.
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PCA often discovers that not all variables need to be linearly associated.
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There is some data loss.
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For LDA to work, labeled data must be present, which isn't always the case.
Techniques for dimensionality reduction consist of the following:
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Generalized Discriminant Analysis (GDA)
One technique for reducing dimensionality is generalized discriminant analysis (GDA). It finds a nonlinear projection that maximizes between-class dissimilarity and decreases within-class dissimilarity to promote class separability.
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Linear Discriminant Analysis (LDA)
Fisher's linear discriminant is a technique used in statistics and other fields to identify a linear combination of features that distinguishes between two or more classes of objects or events. Its generalizations include linear discriminant analysis (LDA), normal discriminant analysis (NDA), and discriminant function analysis.
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Principal Component Analysis (PCA)
A method for lowering the dimensionality of such datasets and improving interpretability while minimizing information loss is principal component analysis (PCA). It accomplishes this by producing new, uncorrelated variables that maximize variance one after the other.
Conclusion
Every second, massive amount of data are produced. Therefore, it is equally crucial to analyze them accurately and with the best available resources. Dimensionality Reduction approaches facilitate precise and effective data pre-processing, which is why they are seen as a blessing for data scientists. Do you also wish to become a data scientist in top MNCs? Then take up the top Data science certification course in Delhi, and leverage the skills learnt in the real-world and capstone projects.
