Predictive modeling is an essential part of machine learning. It involves the development of models that can predict new data and training on historical data. The main concern for many people when it comes to predictive modeling is how they can get better results. The internet has millions of resources that highlight how tech-savvy companies and individuals can make the most out of their machine learning technologies. With the following guide, anyone can lift the performance of their machine learning technology and even achieve exceptional results on their predictive modeling.
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Improve Performance with Big Data
Changes to problem definition and training data can drive an enterprise to big wins. Users of machine learning technology might also need to create different perspectives on their data to expose their underlying problem to the learning algorithms. With big data, you can create a suite of new versions and views of your set of data, and then use predictive modeling algorithms to evaluate the value of each dataset.
Improve Performance with Ensembles
Users of machine learning technologies can use multiple models to combine their predictions. The next area of improvement after algorithm tuning should be a focus on ensembles. Combination of forecasts from several models can help an organization realize better performance instead of relying on several highly fragile and tuned models. Therefore, the best way to achieve high machine learning performance is to combine predictions of various well-performing models. However, machine learning technology users might need to utilize at least one ensembles of well-performing models and ensure no single model can outperform it. After that, users can finalize one or more ensembles and put them into production or use them for prediction purposes.
Utilize Algorithm Tuning for Improved Performance
You might decide to make where you spend much of your time as your algorithm tuning. While this practice can be tedious and time-consuming, users can leverage it to unearth their well-performing algorithms from any spot. However, it might take days, weeks, or even months to get the most out of those well-performing algorithms. With algorithm tuning strategy, users of machine learning technology can get the most out of their predictive models. However, users might need to configure at least one of their highly-tuned algorithms with their machine learning problem. With this, users of machine learning algorithms can use either of their models to make predictions or boost their productivity. For example, Google uses machine learning to predict search keywords and improve result relativity. For any user, a further combination of forecasts from several models can lift the performance of machine learning algorithms.
Users of machine learning technology can utilize the feature engineering technique to extract information from their existing datasets. The feature also comes with a high capability to explain their training data variance and give improved model accuracy. Hypotheses generation can influence feature engineering and can result in useful features. That’s the reason experts recommend users of machine learning technology to invest a lot of time in the generation of hypotheses. You can subdivide feature engineering in either feature creation or feature transformation, where transformation involves the changing of original variables to variables between one and zero. In contrast, feature creation entails the invention of new variables from the existing variables to help unleash how data sets relate to each other.
Treatment of Outlier and Missing Values
The presence of unwanted outlier or missing values could either result in a biased model or reduce the accuracy of a model, which can result in inaccurate prediction. That means it’s crucial to address outlier and missing values thoroughly because users of machine learning technology don’t analyze their relationship and behavior with other variables accurately. Reliance on more data is always an outstanding idea. It allows for data to prove for itself instead of relying on weak correlations and assumptions. The presence of large volumes of data results in accurate and better results than before. However, users of machine learning technology rarely get an opportunity to add more data or increase the size of their training data in various projects.