7 Ways to Improve Machine Model Results

Imagine you have prepared for an exam or competition, but in spite of your efforts you do not get the results you want. If you choose to try again, you will likely revise your preparation tactics based on your first experience. In other words, you will fine tune yourself and your techniques to achieve a better result. A Similar approach is used to improve the scores of machine learning models, and in this article, 7 particular methods of improvement will be discussed.

1. Adding more data

Assuming that it is relevant and accurate, there can never be too much data. When advanced machines receive more data, they learn more, self-correct flaws, and produce more desirable results. This is very much similar to us as the more we refer to different sources to prepare for an exam, the better we perform during the test.

2. Treating missing and outlier values

While more data is always more beneficial, the quality of the data must also be taken into consideration. When there are missing values and outliers present in a dataset, the data can often be more detrimental than helpful for machine learning. As a result, it is crucial to identify and correct them in order for machines to learn accurately. Once again using people as an example, imagine preparing for a biology quiz but the only birds you know are penguins and ostriches. If you disregarded every other species of birds and decided that these two species were emblematic of birds as a whole, it would be logical to assume that no birds fly when in reality flightless birds are outliers. Ultimately, data cannot added at the cost of quality.

3. Feature Engineering

Feature engineering helps machines extract more information from existing information. With knowledge of new data analysis techniques, machines can better explain variance and various patterns present in the data. To put it into simpler terms, when analyzing a dataset, more conclusions will be able to be drawn if one calculates the data’s standard deviation, range, mean, and median as opposed to just the mean.

4. Feature Selection

However, just because a machine has more features does not guarantee it will be able to analyze data better. If machine features generate unimportant information, this will actually prove detrimental to the machine data analysis as a whole. Therefore, feature selection must be optimized for machines to perform desirably. For example, if a machine was given the task of computing the average length of an MLB player’s home-runs, having a mode parameter would only add unnecessary strain to the machine processing.

5. Hyperparameter Tuning

Hyperparameters are used to control the machine model’s learning process, and like the name suggests, hyperparameter tuning is a technique to improve model’s performance that involves modifying model’s hyperparameters for better results. It is similar to trying out different frequencies on a radio to find a particular station when the frequency of the desired radio station is unknown. Like the tuning in the radio analogy, tuning of hyperparameters is very tedious process to complete manually, but luckily there are alternatives to this approach. We can either utilize grid searching which is a process that sifts through manually specified subsets of the hyperparameter space until it finds the targeted algorithm or random searching which selects a value for each hyperparameter independently using a probability distribution.

6. Ensemble Models

Ensemble modeling is a process where multiple diverse models are used in tandem to predict outcomes. While the models are weak on their on, when they team up to complete their machine learning objectives, they produce a strong learner. There are multiple different methods used to combine the models including bootstrapping, bagging, blending, and stacking. For more information on these methods check out the following article.

7. Neural Networks

This is a method where AI is built based on the human nervous system with interconnected “machine neurons” organized layer wise. Like the name suggests, each layer has specified number of neurons, and there is an input layer and an output layer. Between the input and output layer is where all the machine learning and processing actually happens. By building AI in a way that resembles the human brain, AI is able to bounce thoughts through its neural network until it can produce a conclusion.