In machine learning and data sciences, the dimensions are also known as attributes. In most cases, it becomes compulsory to reduce the number of attributes or features from the dataset to train the model more efficiently and accurately. There are many methods used for reducing the features from the data set in machine learning and data science. The data scientists choose the methods according to the needs of the models to be applied to the data set. Sometimes, the model’s accuracy decreases and sometimes increases due to the reduced data set’s attributes. Data scientists have to choose the attributes from the data set very wisely, by which the model’s accuracy does not change and takes less time to train the model. Here we will discuss dimensionality reduction and some prominent techniques to reduce the attributes in a better way. Don’t delay your career growth, kickstart your career by enrolling in this Data Science Training Institute In Chennai
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What is Dimensionality Reduction:
It is the process of reducing or deleting the attributes from the data set and choosing that subset of the features that provides the best accuracy value. Moreover, the selected subset should be trained more quickly as compared to the other feature subsets. While choosing the best attribute subset, model accuracy and efficiency are considered the first priority to train a model. Sometimes, this method is also known as feature selection or feature extraction. Earn yourself a promising career in data science by enrolling in the Data Science Classes in Pune offered by 360DigiTMG.
Why Dimensionality Reduction is Important in Data Science:
In most cases, the training data size is enormous; it becomes difficult to train the model with a regular computer CPU. Moreover, when the training data set’s size increases, it takes more time to train the model. On the other hand, the model’s accuracy or performance is much disturbed, with an extensive training data set. The attributes of the data set are reduced to solve all these problems. Using the dimensionality reduction, only that features are chosen from the data that can enhance the model’s accuracy and performance. The selected data subset should also represent the original data set. Want to learn more about data science? Enroll in the data science course in pune with placement guarantee to do so.
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What is the Curse Of Dimensionality Problem In Data Sciences:
In Data sciences and machine learning, when the algorithms are not scaling the data correctly due to more dimensions or features in the data set, then the data scientists call this problem the curse of dimensionality. Due to more dimensions in the data set, the data set model requires more memory and time to train the data. Looking forward to becoming a Data Scientist? Check out the Data Science Course With Placement and get certified today.
For Which Type of Problems Dimensionality Reduction Technique Is Useful:
The dimensionality reduction technique is useful for both classification and regression problems in machine learning and data sciences. In most cases, it is the prerequisite to reduce the dimensions for some classification and regression algorithms regardless of the memory and training time requirements. Looking forward to becoming a Data Scientist? Check out the data science training in bangalore and get certified today.
We have discussed the prominent data preprocessing technique, which is essential for training many machine learning and data science algorithms. For more articles related to data sciences, please keep visiting our website.
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