A machine learning model is trained to predict customer churn for a telecom company. The model achieves high accuracy during training but performs poorly when applied to new, unseen data. What could be the most likely cause of this issue?a)Inappropriate choice of evaluation metricb)Insufficient training datac)Underfittingd)Overfitting
Question
A machine learning model is trained to predict customer churn for a telecom company. The model achieves high accuracy during training but performs poorly when applied to new, unseen data. What could be the most likely cause of this issue?a)Inappropriate choice of evaluation metricb)Insufficient training datac)Underfittingd)Overfitting
Solution
The most likely cause of this issue is d) Overfitting.
Overfitting occurs when a machine learning model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new, unseen data. This means the model is not generalized well and is too complex. It has learned the 'noise' or random fluctuations in the training data as concepts, which do not apply to new data and hence, the model performs poorly on it.
While a) Inappropriate choice of evaluation metric, b) Insufficient training data, and c) Underfitting could also potentially cause poor performance on unseen data, the scenario described - where the model performs well on training data but not on new data - is most indicative of overfitting.
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Customer churn is a major problem and one of the most important concerns for large companies. Due to the direct effect on the revenues of the companies, especially in the telecom field, companies are seeking to develop means to predict potential customer churn. Therefore, finding factors that increase customer churn is important to take necessary actions to reduce this churn. The main contribution of your work is to develop a churn prediction model that assists telecom operators in predicting customers who are most likely subject to churn. Perform the following operations as you create the much needed deep learning application.Using the given datasetLinks to an external site. extract the relevant features that can define a customer churn.Use your EDA(Exploratory Data Analysis) skills to find out which customer profiles relate to churning a lot.Using the features in (1) define and train a Multi-Layer Perceptron modelEvaluate the model’s accuracy and calculate the AUC scoreCreate a platform to host the model either web-based or desktop applicationAllow users to use the application to enter new data and your model should predict if the supplied data of a new customer can result in a churn or not giving the confidence factor of the model
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