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After training and evaluating your machine learning model in Mostly AI, you're ready to put it to work and generate predictions on new, unseen data. This is where the power of your machine learning model comes into play, enabling you to make informed decisions and solve real-world problems.

  Go to the "Predictions" section within your project's workspace.

 Choose the trained model you want to use for generating predictions. 

 Prepare the new data you want to make predictions on.  This data should have the same format and features as the data you used to train your model. 

 Initiate the prediction process.  Mostly AI will process your new data and generate predictions based on your trained model. 

   View the generated predictions in a tabular format or download them for further analysis. 

 The output will be numerical values, representing predictions for the target variable.

 The output will be class labels, indicating the predicted category for the input data.

  Many models provide confidence scores or probabilities associated with each prediction, indicating the model's confidence in its prediction.

 Carefully evaluate the predicted outputs, considering the confidence scores and the context of your problem.

 Use the generated predictions to make informed decisions in your domain.  For example, you might use a fraud detection model to flag suspicious transactions or a customer churn model to identify at-risk customers.

  Integrate predictions into automated processes, such as triggering alerts, creating personalized recommendations, or triggering actions in your business workflows.

  Monitor the performance of your predictions in real-world applications. If necessary, re-train your model or refine your data to improve accuracy and effectiveness. 

By understanding the process of generating predictions and interpreting their output, you can effectively leverage your trained models in Mostly AI to unlock valuable insights and make informed decisions that drive better outcomes.

A clear explanation on how to feed new data to your trained model and generate predictions.

Generating Predictions with Your Trained Model

In Mostly AI, you have the flexibility to choose between two prediction modes: batch prediction and real-time prediction. Understanding the differences between these modes is crucial for making informed decisions about how to apply your trained model effectively.

 Batch prediction involves generating predictions for an entire dataset at once. 

 You provide the model with a set of data as input and receive predictions for all data points in the batch.

  Batch prediction is computationally efficient, as the model can process the entire dataset in a single operation.

 Real-time prediction generates predictions for individual data instances as they arrive.

  You feed individual data points to the model and receive predictions in real-time.

  Real-time prediction prioritizes low latency, making it suitable for applications where quick responses are critical.

Consider the following factors when deciding between batch and real-time prediction:

 For large datasets, batch prediction is typically more efficient.

  If predictions need to be generated immediately, real-time prediction is necessary. 

  Real-time prediction might require more computational resources, especially if you are handling high volumes of data.

  The specific requirements of your application will dictate the most appropriate prediction mode.

  Generating customer churn predictions for an entire customer base to identify at-risk customers for targeted marketing campaigns.

  Using a fraud detection model to analyze transactions in real-time and flag suspicious activity. 

By understanding the differences and benefits of batch and real-time prediction, you can select the optimal mode for your machine learning application within Mostly AI.

Explore the different modes of prediction in Mostly AI – batch prediction for large datasets and real-time prediction for individual instances.

Batch Prediction vs. Real-Time Prediction

When generating predictions with your trained machine learning model in Mostly AI, you'll often encounter prediction probabilities or confidence scores associated with each output. These values provide valuable insights into the model's certainty about its predictions, helping you to interpret results more effectively.

 For classification tasks, prediction probabilities indicate the model's estimated likelihood that a given data point belongs to a particular class.  

  Higher probabilities suggest stronger confidence in the predicted class. However, it's crucial to consider the context of your problem and the overall accuracy of your model when interpreting probabilities.

 Confidence scores are often provided for both classification and regression models.

  Confidence scores generally represent the model's level of certainty about its prediction, often expressed as a percentage or a numerical value on a scale. 

Utilizing Probabilities and Confidence Scores

  When making decisions based on predictions, consider both the predicted output and the associated probabilities or confidence scores. Higher confidence suggests more reliable predictions.

  Set thresholds for probabilities or confidence scores to define decision boundaries.  For example, you might only take action if the probability of a customer churning exceeds 80%.

  Recognize that even with high probabilities or confidence scores, predictions still carry inherent uncertainty.  Consider factors like model accuracy and data quality when interpreting results.

**Imagine you're using a customer churn prediction model:**

 The model predicts that a specific customer will churn with a probability of 0.65.

  This suggests a moderately high chance of churning.

  Based on this prediction and the associated probability, you might decide to take action to try and retain the customer, such as offering a discount or providing personalized support.

By understanding and utilizing prediction probabilities and confidence scores, you can make more informed decisions and interpret your model's predictions more effectively within Mostly AI.

Interpreting the output of your model, including probabilities and confidence scores for each prediction.