How can machine learning be used to improve the accuracy of predictions?
Machine Learning
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Machine learning can be an extremely useful tool when it comes to designing and developing algorithms, especially those that learn from and make predictions on data. It has already begun to be adapted across diverse industries for uses such as spam filtering, image recognition and medical diagnosis. And making more accurate predictions, in turn, can help professionals make better decisions. Here are some ways machine learning can be used to improve the accuracy of predictions.
1. Using more data: The more data an algorithm has to learn from, the better it can identify its underlying patterns. For instance, a larger dataset may be more representative of the overall population, so the machine learning algorithm will know more about a wider, more diverse demographic. Additionally, using more data can also help to reduce the amount of noise and error in predictions.
2. Using better features and parameters: Features are the inputs you select for your algorithm. Good features are those that are relevant to the task at hand and can be easily learned by the algorithm. Machine learning can efficiently build algorithms with features best suited for your task. For example, when predicting whether an email is spam, a machine learning algorithm will likely select features like subject line and the sender’s email address. Machine learning can also better tune the parameters of the algorithm, which control how it learns and, in turn, makes predictions.
“In general, we want to have the lowest bias and variance possible, but in most cases, you can’t decrease one without increasing the other - this is called the bias-variance trade-off. To work around this: Know the desired outcome from your model, choose the appropriate ML algorithm and configure it correctly by adjusting hyper-parameters.”
— Amey Dharwadker is a machine learning lead at Facebook.
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3. Using more sophisticated algorithms: Machine learning algorithms can be more sophisticated than traditional algorithms. They can handle larger and more complex datasets and find meaningful patterns. More sophisticated algorithms can account for a greater number of variables, and they can more accurately model nonlinear relationships. They can also better handle missing data and outliers, both of which can impact the accuracy of predictions.
“[Machine learning] can recognize complex patterns and similarities/differences among customers that are more meaningful and deliver higher ROIs. The output enables the machine to learn and make good decisions rather than supplying an ‘insight’ that can be articulated in the way we were used to.”
— Kathy Schaller is the VP of strategy and consulting at data science company FocusKPI. She holds over 30 years of experience in consulting and marketing and earned her MBA from the Tuck School of Business at Dartmouth.
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This article is a beta test. It was created by having an AI generate an initial answer to a question. The response was then fact checked, corrected, and amended by editor Felicia Hou . Any errors or additions? Please let us know in the comments.
Chief Science Officer | USAF Veteran | Former LEO
2yMachine learning is an accelerator to insight. When coupled with appropriate subject matter experts, it becomes a powerful information tool. The key is to continually validate outputs and make refinements where needed.
Software Engineer at Netflix
2yAnother aspect not being discussed here is the quality of training data. More data is not always good. In some scenarios where we use auto labeling, ensuring a high quality of labeling becomes important for the model to have high quality predictions. Also when we are thinking of actual real time systems where we use ML models, the correlation between offline metrics used to measure the quality of the model preproduction and the online metrics which we are finally trying to optimize on becomes critical in speed of innovation. Offline metrics usually take the form of precision/recall of the prediction while online metrics are some form of user interaction weighted formula. If either metric does not perform what we intend for it to, no form of modeling can help explain why predictions are not getting better.
Computer Vision Engineer at Qualcomm
2yMachine learning can be used as an efficient tool to capture underlying trends in data when it may be infeasible to come up with analytical solutions to problems. In such cases, machine learning techniques can be used in conjunction with domain knowledge to make accurate predictions.
Oracle Tech Manager - Cloud || PaaS || OIC
2yThe key to a successful Machine Learning process is how effective and robust the underlying model is. There are few key considerations in defining the model: Size of the model: This is driven by the number of predictors used for building a model. The model size needs to be trimmed down to obtain a goodness-of-fit. This means while selecting models - if one were to choose the largest model with high number of predictors, there are chances one would end up fitting to noise. Choosing the right predictors: While some predictors could be identified as useful in model building. These predictors can have positive effect when used just by itself (or) with other uncorrelated predictors. However, it can impose a negative effective effect when used in conjunction with another highly correlated predictor(s). In worst cases it could lead to collinearity and can have adverse side effects - increasing the algorithm's complexity, induce unknown errors and affect reliability of the model.
Machine Learning Engineer | Software Engineer | Author
2yApart from the solutions mentioned in the article, what's really important is understanding the data and its correlation with the prediction you want to make. More data may not always be available and you may not have enough computing capacity to run sophisticated algorithms. So, understanding data distribution, carefully reducing dimensionality, selecting correct features and then selecting models is another way to improve accuracy. Sometimes all we have is a nail and a simple hammer will work fine for it, no need to have a sledgehammer.