In simpler terms, normalization makes sure that all of your data looks and reads the same way across all records. Normalization will standardize fields including company names, contact names, URLs, address information streets, states and cities , phone numbers and job titles. Every company has different criteria when it comes to normalizing their data. One of the biggest impacts of normalizing your data is reducing the number of duplicates in your database.
Anotherbenefit of normalizing your data is that it will help your marketing team segment leads, particularly with job titles. Job titles vary greatly among companies and industries, making it nearly impossible to associate a given job title with anything really actionable for segmentation or lead scoring.
So standardizing this value can be very useful, and a number of approaches are possible. For example, you can use a lookup list approach in a recent engagement. One of the most notable is the fact that data normalization means databases take up less space. A primary concern of collecting and using big data is the massive amount of memory needed to store it. As such, finding ways to decrease disk space is a priority, and data normalization can do that.
Taking up less disk space is great on its own, but that also has the effect of increasing performance. The benefits of data normalization go beyond disk space and its related effects. Many organizations use the data in their database to look at how to improve their organization. This can become a complex task especially if the data they have comes from multiple sources.
Perhaps a company has a question about sales numbers that relates to social media engagement with customers. The data comes from different sources, so cross-examining them can be challenging, but with data normalization, that process is easier. If you use a variety of Software-as-a-Service applications, for example, you can consolidate and query data from those applications with ease.
If you need to export your logs from a location, then you can do so without having any repeated data values. You can visualize data from any business intelligence tools you have along with reports and analytics platforms. To go along with those benefits, data normalization can also be of great use to certain people. The same goes for those who need to perform statistical modeling for the data they have as part of their job.
In other words, data scientists and business analysts have a lot to gain from using the data normalization process. Do you spend a lot of your time working with business models? You may benefit from this process as well. The same goes for those who work with database maintenance, ensuring everything is running smoothly on that front.
In fact, pretty much anyone involved in data and analysis will find data normalization to be extremely useful. The picture below could be [roughly] viewed as the example of an elongated error surface in which the gradient-based methods could have a hard time to help the weight vectors move towards the local optima. I was trying to classify a handwritten digits data it is a simple task of classifying features extracted from images of hand-written digits with Neural Networks as an assignment for a Machine Learning course.
I tried changing number of layers, the number of neurons and various activation functions. None of them yielded expected results accuracy around 0. The Culprit? If the parameter s is not set, the activation function will either activate every input or nullify every input in every iteration.
Which obviously led to unexpected values for model parameters. My point is, it is not easy to set s when the input x is varying over large values. As some of the other answers have already pointed it out, the "good practice" as to whether to normalize the data or not depends on the data, model, and application.
By normalizing, you are actually throwing away some information about the data such as the absolute maximum and minimum values. So, there is no rule of thumb. IOW: you need to have all the data for all features before you start training. Many practical learning problems don't provide you with all the data a-priori, so you simply can't normalize.
Such problems require an online learning approach. They learn the scales and compensate for them, iteratively. Most of the time this corresponds to applying an affine function. Now, suppose you want to predict for some new sample. This makes these matrices have the same form as other parameters in your model. While they are often equivalent in terms of the predicted values you get from the training dataset, it certainly isn't on new data for predictions.
For machine learning models that include coefficients e. Computationally, your predictors are often transformed by the learning algorithm e. If your predictors are on the scale of 's then this won't matter. If you're modeling grains of sand, astronomical units, or search query counts then it might.
I was trying to solve ridge regression problem using gradient descent. Now without normalization I set some appropriate step size and ran the code. In order to make sure my coding was error-free, I coded the same objective in CVX too. Now CVX took only few iterations to converge to a certain optimal value but I ran my code for the best step size I could find by 10k iterations and I was close to the optimal value of CVX but still not exact. After normalizing the data-set and feeding it to my code and CVX, I was surprised to see that now convergence only took iterations and the optimal value to which gradient descent converged was exactly equal to that of CVX.
Also the amount of "explained variance" by model after normalization was more compared to the original one. So just from this naive experiment I realized that as far as regression problem is concerned I would go for normalization of data. BTW here normalization implies subtracting by mean and dividing by standard deviation. Sign up to join this community. The best answers are voted up and rise to the top. Stack Overflow for Teams — Collaborate and share knowledge with a private group.
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