It compresses the data, which reduces the computation time and facilitates faster training of the data.It reduces the amount of storage space required (less data needs lesser storage space).It facilitates the usage of algorithms that are unfit for more substantial dimensions.
It helps improve the model’s accuracy and performance.It eliminates noise and redundant features.A training data with considerably lesser features will ensure that your model remains simple – it will make smaller assumptions.Īpart from this, dimensionality reduction has many other benefits, such as: The primary aim of dimensionality reduction is to avoid overfitting. This will result in an overfitted model that fails to perform well on real data. When you train an ML model on a large dataset containing many features, it is bound to be dependent on the training data. When the number of variables increases, the model will become more complex, thereby increasing the likelihood of overfitting. So, if you think every variable within the feature set, you will include many redundant factors in the training set.įurthermore, the more variables you have at hand, the higher will be the number of samples to represent all the possible combinations of feature values in the example. Another vital point to consider is that most of the variables are often correlated. variables) in a feature set, the more difficult it becomes to visualize the training set and work on it. The higher is the number of features or factors (a.k.a. The curse of dimensionality is a phenomenon that arises when you work (analyze and visualize) with data in high-dimensional spaces that do not exist in low-dimensional spaces. The curse of dimensionality mandates the application of dimensionality reduction. Now comes the question, why must you reduce the columns in a dataset when you can directly feed it into an ML algorithm and let it work out everything by itself? By applying dimensionality reduction, you can decrease or bring down the number of columns to quantifiable counts, thereby transforming the three-dimensional sphere into a two-dimensional object (circle). Usually, machine learning datasets (feature set) contain hundreds of columns (i.e., features) or an array of points, creating a massive sphere in a three-dimensional space. In simple words, dimensionality reduction refers to the technique of reducing the dimension of a data feature set. What are the disadvantages of dimensionality reduction?.What are ways of reducing dimensionality?.