Replication Data for: Comparative performance analysis of three machine learning algorithms applied to sensor data in dairy cattle to predict metritis events II. Behaviors measured with a leg-attached accelerometer
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Dataset containing all the performance metrics (sensitivity, specificity, positive and negative predictive values, accuracy, and F1 score) using a rank-based approach and  three different decision thresholds (highest 20%, 30%, and 40% class probabilities). Each row is a model that is defined by the combination of classifier (k-NN, RF, SVM), type of behavior measured with the sensor device (lying time, lying bouts, steps, intake, intake visits), time of the day corresponding to the sensor data used (all day, evening-night), parity (all cows regardless of parity, primiparous, multiparous), time window to aggregate sensor data (24, 12, 6, 3 hours), and number of time steps before a given event used as model inputs as a function of the time window used to aggregate sensor data. Also, information regarding number of metritis events, number o' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["classification algorithms","dairy cattle behavior","machine learning","precision dairy technology","predictive modeling","supervised learning"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Dataset containing all the performance metrics (sensitivity, specificity, positive and negative predictive values, accuracy, and F1 score) using a rank-based approach and  three different decision thresholds (highest 20%, 30%, and 40% class probabilities). Each row is a model that is defined by the combination of classifier (k-NN, RF, SVM), type of behavior measured with the sensor device (lying time, lying bouts, steps, intake, intake visits), time of the day corresponding to the sensor data used (all day, evening-night), parity (all cows regardless of parity, primiparous, multiparous), time window to aggregate sensor data (24, 12, 6, 3 hours), and number of time steps before a given event used as model inputs as a function of the time window used to aggregate sensor data. Also, information regarding number of metritis events, number o' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Dataset containing all the performance metrics (sensitivity, specificity, positive and negative predictive values, accuracy, and F1 score) using a rank-based approach and  three different decision thresholds (highest 20%, 30%, and 40% class probabilities). Each row is a model that is defined by the combination of classifier (k-NN, RF, SVM), type of behavior measured with the sensor device (lying time, lying bouts, steps, intake, intake visits), time of the day corresponding to the sensor data used (all day, evening-night), parity (all cows regardless of parity, primiparous, multiparous), time window to aggregate sensor data (24, 12, 6, 3 hours), and number of time steps before a given event used as model inputs as a function of the time window used to aggregate sensor data. Also, information regarding number of metritis events, number o
classification algorithms; dairy cattle behavior; machine learning; precision dairy technology; predictive modeling; supervised learning