Difference between revisions of "Dimensional Reduction"
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| + | * [http://files.knime.com/sites/default/files/inline-images/knime_seventechniquesdatadimreduction.pdf Seven Techniques for Dimensionality Reduction | KNIME] | ||
Some datasets may contain many variables that may cause very hard to handle. Especially nowadays data collecting in systems occur at very detailed level due to the existence of more than enough resources. In such cases, the data sets may contain thousands of variables and most of them can be unnecessary as well. In this case, it is almost impossible to identify the variables which have the most impact on our prediction. Dimensional Reduction Algorithms are used in this kind of situations. It utilizes other algorithms like Random Forest, Decision Tree to identify the most important variables. [http://towardsdatascience.com/10-machine-learning-algorithms-you-need-to-know-77fb0055fe0 10 Machine Learning Algorithms You need to Know | Sidath Asir @ Medium] | Some datasets may contain many variables that may cause very hard to handle. Especially nowadays data collecting in systems occur at very detailed level due to the existence of more than enough resources. In such cases, the data sets may contain thousands of variables and most of them can be unnecessary as well. In this case, it is almost impossible to identify the variables which have the most impact on our prediction. Dimensional Reduction Algorithms are used in this kind of situations. It utilizes other algorithms like Random Forest, Decision Tree to identify the most important variables. [http://towardsdatascience.com/10-machine-learning-algorithms-you-need-to-know-77fb0055fe0 10 Machine Learning Algorithms You need to Know | Sidath Asir @ Medium] | ||
Revision as of 08:50, 22 June 2018
Some datasets may contain many variables that may cause very hard to handle. Especially nowadays data collecting in systems occur at very detailed level due to the existence of more than enough resources. In such cases, the data sets may contain thousands of variables and most of them can be unnecessary as well. In this case, it is almost impossible to identify the variables which have the most impact on our prediction. Dimensional Reduction Algorithms are used in this kind of situations. It utilizes other algorithms like Random Forest, Decision Tree to identify the most important variables. 10 Machine Learning Algorithms You need to Know | Sidath Asir @ Medium
