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Validation: Difference between revisions
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(Created page with "{{see also|Machine learning terms}} ===Introduction== Machine learning practitioners understand the importance of validation as one of the key steps in developing a predictive model. Validation measures the accuracy and dependability of a trained model by applying it to new data sets, with an aim of estimating its likely performance when applied. ==Training and Testing Data== Validating a machine learning model requires labeled data that can be used for training and tes...") |
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{{see also|Machine learning terms}} | {{see also|Machine learning terms}} | ||
==Introduction== | |||
Validation checks the quality of the [[model]]'s predictions by testing the model against the new [[data]] in [[validation set]]. Validating a [[machine learning model]] requires [[labeled data]] that can be used for [[training]] and [[testing]]. Usually, a [[dataset]] is divided into 3 sets: a [[training set]], [[validation set]] and [[test set]]. The training set of data instructs the model how to [[classify]] or predict outcomes based on [[input data]], while the validation set evaluates the model's [[accuracy]] and performance. Validation prevents the model from [[overfitting]] to the training set. Validation can be thought of as the first around of testing and evaluating the model while [[test set]] is the 2nd round. | |||
Validating a machine learning model requires labeled data that can be used for training and testing. Usually, | |||
==Validation Methods== | ==Validation Methods== | ||
Validating a model requires different approaches, each with their own advantages and drawbacks. Three common techniques for validation are k-fold cross validation, hold-out validation, and leave-one-out validation. | Validating a model requires different approaches, each with their own advantages and drawbacks. Three common techniques for validation are [[k-fold cross validation]], [[hold-out validation]], and [[leave-one-out validation]]. | ||
===k-Fold Cross-Validation== | ===k-Fold Cross-Validation=== | ||
K-fold cross validation (kFCV) is a popular technique that involves splitting the data into k equal subsets. One subset serves as the testing set, while the remaining k-1 subsets train the model. This cycle repeats itself k times with each subset being tested once. After averaging these results, an estimate of their accuracy can be made. | K-fold cross validation (kFCV) is a popular technique that involves splitting the data into k equal subsets. One subset serves as the testing set, while the remaining k-1 subsets train the model. This cycle repeats itself k times with each subset being tested once. After averaging these results, an estimate of their accuracy can be made. | ||
===Hold-Out Validation== | ===Hold-Out Validation=== | ||
Hold-out validation involves dividing the data into training and testing sets. Usually, a large portion of this information goes toward training the model, while the remainder serves for testing. While this approach is straightforward and straightforward to execute, it may not provide an accurate representation of model performance if the testing set is too small or not representative of all available information. | Hold-out validation involves dividing the data into training and testing sets. Usually, a large portion of this information goes toward training the model, while the remainder serves for testing. While this approach is straightforward and straightforward to execute, it may not provide an accurate representation of model performance if the testing set is too small or not representative of all available information. | ||
===Leave-One-Out Validation== | ===Leave-One-Out Validation=== | ||
Leave-one-out validation involves training the model on all but one data point and testing it on the remaining one. This process is repeated for each data point in the set, with results then averaged. This approach works best when working with small datasets but may prove computationally expensive for larger ones. | Leave-one-out validation involves training the model on all but one data point and testing it on the remaining one. This process is repeated for each data point in the set, with results then averaged. This approach works best when working with small datasets but may prove computationally expensive for larger ones. | ||
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Once a validation method is selected, its performance is assessed using several metrics. These include accuracy, precision, recall, F1 score and area under the receiver operating characteristic curve (AUC-ROC). | Once a validation method is selected, its performance is assessed using several metrics. These include accuracy, precision, recall, F1 score and area under the receiver operating characteristic curve (AUC-ROC). | ||
===Accuracy== | ===Accuracy=== | ||
Accuracy is the percentage of correctly classified instances within a testing set. It provides an easy-to-understand gauge of a model's performance. | Accuracy is the percentage of correctly classified instances within a testing set. It provides an easy-to-understand gauge of a model's performance. | ||
===Precision and Recall== | ===Precision and Recall=== | ||
Precision measures the percentage of true positive predictions among all positive predictions, while recall evaluates the proportion of true positives among actual positives. Precision and recall are often combined to assess a model's performance when there is an imbalance in class size. | Precision measures the percentage of true positive predictions among all positive predictions, while recall evaluates the proportion of true positives among actual positives. Precision and recall are often combined to assess a model's performance when there is an imbalance in class size. | ||
===F1 Score== | ===F1 Score=== | ||
The F1 score is the harmonic mean of precision and recall. It can be an useful metric when both precision and recall are important factors. | The F1 score is the harmonic mean of precision and recall. It can be an useful metric when both precision and recall are important factors. | ||
===AUC-ROC== | ===AUC-ROC=== | ||
AUC-ROC is a measure of a model's capability to discriminate between positive and negative instances. It's calculated as the area under the curve on an ROC plot. A model with a higher AUC-ROC value will be better at discriminating between positive and negative instances. | AUC-ROC is a measure of a model's capability to discriminate between positive and negative instances. It's calculated as the area under the curve on an ROC plot. A model with a higher AUC-ROC value will be better at discriminating between positive and negative instances. | ||