Machine Learning is the science of finding patterns and trends in complicated, real-world data. Machine Learning can unlock the value hidden in data, whether those data sets are large, high dimensional, heterogeneous, or unstructured. The automated nature of Machine Learning applications enables people to make optimized, data-driven decisions in real-time. Our Machine Learning applications are built on the three pillars of accuracy, interpretability and implementability.
In business, accuracy matters. Wise Applications learn from your data, so you always get the most accurate answers tuned to your specific business questions.
We don't believe in black box models, nor should you. Wise Applications give highly interpretable insight, so you know precisely which factors drive predictions.
Wise Applications are designed to easily be implemented and placed into production. Don't let your work get stuck as a prototype.
Learn more about the world class technology used in our applications
Putting machine learning into production is essential. Our core machine learning technologies enable our highly scalable applications to be built and configured at lightning speed, producing predictive insights that are both accurate and interpretable. Learn more in our whitepaper.
Wise Applications utilize modern web technologies paired with the state-of-the-art Wise Machine Learning infrastructure.
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Machine Learning is good at replacing labor-intensive decision-making systems that are predicated on hand-coded decision rules or manual analysis.
You can think of Machine Learning as “predictive analytics on steroids.” Whereas predictive analytics has historically relied on restrictive, so-called parametric models (such as linear regression & logistic regression), Machine Learning employs truly data-driven models that can extract more knowledge from your data.
A training set of data and an interesting business problem is all that is needed. The training set should consist of a set of events or objects for which the outcome of interest is known plus any relevant input data that may be predictive of that outcome.
A set of data whose outcome variable is known. Training data are used to build a supervised ML model. Training data can further be split into separate training and testing data sets, where the latter is used to validate the performance of the model. This step allows us to estimate what the accuracy of the model will be on future data and thus ensure that production-grade standards will be met.
Feature extraction is the process of extracting additional information out of your raw data. A simple example of feature extraction is taking a datetime input and expanding it into an enriched vector of information such as second, minute, hour, week, month, quarter and year.
Predictive models. These algorithms require a training set of data with known outcomes, and yield an optimized decision-making process. Unsupervised Machine Learning is mainly used for exploratory analysis (finding natural clusters, patterns, or outliers in the data) or visualization (through dimensionality reduction).
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