Dimensionality Reduction in Data Science
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With data sciences becoming more popular, data science has become more widely used. Data scientists are still working on a sizable proportion of the challenging issues they have already addressed with data science. Data science is also being used by researchers in several domains. Data science is also being used by scholars in other domains to analyse data. Data is growing every day as a result of technological advancements in digital systems. Different big data technologies, like Hadoop and Spark, are used to manage the enormous amount of data. The number of characteristics in the data collection represents the dimensions of the data.
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With the increased attributes or dimensions, it becomes more difficult to apply any data science or machine learning model to the data set. Moreover, with the increased attributes, the size of the data set increases; as a result, storage issues also arise. The accuracy and performance of different machine learning models are also disturbed by the increased number of dimensions. To solve this issue, data set dimensions are reduced from the data set to increase the performance of the machine learning model to be applied. Moreover, by reducing the data attributes, the size of the data set also becomes reasonable, and the data scientists can solve the issues of the storage as well. Our team of experts has discussed the data dimensionality techniques in detail.
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What is Data Dimensionality?
Data dimensionality is simply the process of removing the less significant characteristics from the data collection in order to reduce the number of features in the data set. The data scientists choose the subset of data characteristics for the dimensionality reduction approach that best captures the whole set of data attributes. The easiest technique to choose the appropriate subset by evaluating the model's accuracy is often by selecting several subsets of the features. When selecting the qualities of a data collection to feed a data science model, the accuracy and performance of the model are very important. The performance of the model may be improved by employing alternative data science features, and it can occasionally be decreased by selecting other qualities from the data collection. The data scientist is always concerned with selecting the optimal data set characteristics by comparing model correctness.
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Why Model Accuracy Matters a Lot in Dimensionality Reduction?
The data scientist is always concerned about choosing the data set subset, which can be trained more easily by the data science model. So, data science models cannot be feed on the bigger data sets or data sets containing more attributes. It is also necessary to check out the accuracy and performance of the model by selecting different subsets of the dimensions. It is necessary to check each dimensions' importance for the target variable just to consider the accuracy of the data science model. It is observed in different data science practices that by changing a single attribute of the data set in a subset of attributes, the data science model accuracy abruptly increases or decreases. So, it is necessary to check out the importance of each data set attribute and its relation with the target variable or attribute. Data scientists are always concerned about choosing the best attributes out of the whole data attributes set. There are different data science methods for choosing the best attributes from the set of attributes. Some data scientists use statistical formulas to choose the best attributes for feeding the data science model.
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Curse of Dimensionality
The issue that appears while feeding the data science model is known as the curse of dimensionality. You must create additional numbers if subsets of the data set attribute if the data set contains more dimensions or data attributes. The term "curse of dimensionality" is used to describe this issue. The likelihood of over fitting rises as the data science model grows more complicated as there are more number data characteristics. The accuracy of the data science model declines as it is evaluated on fresh data sets when it is over-fitted.
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To remove the issue of over fitting, the data scientists delete some of the attributes from the given data set before training the model. Before training the model, checking the importance of the data set attributes is necessary. Let's take an example of student data set in which we have to find out whether the student can get admission to the specific university or not. Let say this data set has the attributes, name, ages, Id Card number, matric marks, intermediate marks, and marks of entry test. In this data set, there is a total 5 number of attributes or dimensions in the given data set. Let’s say we apply the regression model on this data set to predict whether the student can get admission to the university or not. For this purpose, if we train the model on the complete attributes, there will be errors in the given data set. To train the data model without any errors and over fitting, we have to reduce the dimensions of the data set. For this purpose, we can delete two attributes from the data model, which includes age and the Id card number. By reducing these attributes, there will be no effete on the given target attribute, which is admission to the university. Most universities calculate admission merit by using different criteria. Universities take the matric marks, intermediate marks, and entry test marks to calculate the merit. These attributes are directly concerned with merit and university admission. The age and the Id card have nothing to do with the admission or merit, So, these attributes are of no use and can be discarded.
The data scientists must similarly choose which qualities are more important for supplying the data model. To increase the model's performance and accuracy, the less important features should be eliminated. The specialists on our team have talked about the many facets of data dimensionality. Visit our website often to see more articles on data science.
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