Examples of using Predictive model in English and their translations into Chinese
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By refining our predictive model, we will be able to rapidly assess an individual's tolerance for virtual reality and tailor their experience accordingly.
So anybody could say‘build me a predictive model' and it goes off and does it.”.
Below are the some of the standard practices involved to understand, clean and prepare your data for building your predictive model.
The outcome tells the predictive model if the data record represents a customer who did or did not churn.
The desire to build the most predictive model(for example, lowest loss).
The aim is to build a predictive model and find out the sales of each product at a particular store.
Luckily, a standard called PMML(Predictive Model Markup Language) exists that allows predictive models to easily move between different systems.
Your predictive model turned out to be the most accurate, so we wanted to ask for your help in solving this problem.
The objective is to build a predictive model and find out the sales of each product at a particular store.
A good predictive model is able to generalize its knowledge to compute the churn risk even for customers it has never encountered before.
In following the predictive model of Vidal's paper, we would then check through our voluminous pulsar data to see how such a prediction fares.
Also, it identifies both anomalies and cross-selling opportunities and enables users to apply a different predictive model based on their need.
A predictive model that is based on“normal”(that is, nominal control) system behavior is usually required to detect anomalous system behavior.
Following that, analytics methods such as statistics and machine learning are used to produce an“analytic”- a predictive model of your system.
Wikipedia and Twitter have also been added to the predictive model of the AI system.
Also, it recognises both anomalies and cross-selling possibilities and empowers users to apply a distinct predictive model based on their requirement.
Data scientists need to know the intricate details related to stats, machine learning, and math to help build a flawless predictive model.
The Machine Learning process involves building a Predictive model that can be used to find a solution for a Problem Statement.
In this case, some labeled data specific to the target domain remains necessary in order to induce an objective predictive model for the target domain.
A predictive model that performs well with test data but cannot be implemented is useless.