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File Life: 30 days after no activity. No ridiculous queues! No limits!Example: true importance optional Boolean,default is false Whether field importance scores are added as additional columns for each input field. All the fields in the dataset Specifies the fields in the dataset to be considered to create the batch anomaly score. Example: "my new anomaly score" newline optional String,default is "LF" The new line character that you want to get as line break in the generated csv file: "LF", "CRLF".

Example: "Anomaly Score" separator optional Char,default is "," The separator that you want to get between fields in the generated csv file. For example, to create a new batch anomaly score named "my batch anomaly score", that will not include a header, and will only output the field "000001" together with the score for each anomaly score.

Once a batch anomaly score has been successfully created it will have the following properties.

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Creating a batch anomaly score is a process that can take just a few seconds or a few hours depending on the size of the dataset used as input and on the workload of BigML's systems. The batch anomaly score goes through a number of states until its finished. Through the status field in the batch anomaly score you can determine when it has been fully processed.

Once you delete a batch anomaly score, it is permanently deleted. If you try to delete a batch anomaly score a second time, or a batch anomaly score that does not exist, you will receive a "404 not found" response. However, if you try to delete a batch anomaly score that is being used at the moment, then BigML. To list all the batch anomaly scores, you can use the batchanomalyscore base URL.

By default, only the 20 most recent batch anomaly scores will be returned.

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You can get your list of batch anomaly scores directly in your browser using your own username and API key with the following links. You can also paginate, filter, and order your batch anomaly scores. Batch Topic Distributions Last Updated: Monday, 2017-10-30 10:31 A batch topic distribution provides an easy way to compute a topic distribution for each instance in a dataset in only one request.

Batch topic distributions are created asynchronously. You can also list all of your batch topic distributions. You can easily create a new batch topic distribution using curl as follows.

All the fields in the dataset Specifies the fields in the dataset to be considered to create the batch topic distribution. Example: "my new batch topic distribution" newline optional String,default is "LF" The new line character that you want to get as line break in the generated csv file: "LF", "CRLF". For example, to create a new batch topic distribution named "my batch topic distribution", that will not include a header, and will only output the field "000001" together with the probability for each topic distribution.

Once a batch topic distribution has been successfully created it will have the following properties.

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Creating a batch topic distribution is a process that can take just a few seconds or a few hours depending on the size of the dataset used as input and on the workload of BigML's systems. The batch topic distribution goes through a number of states until its finished.

Through the status field in the batch topic distribution you can determine when it has been fully processed. Once you delete a batch topic distribution, it is permanently deleted. If you try to delete a batch topic distribution a second time, or a batch topic distribution that does not exist, you will receive a "404 not found" response.

However, if you try to delete a batch topic distribution that is being used at the moment, then BigML. To list all the batch topic distributions, you can use the batchtopicdistribution base URL. By default, only the 20 most recent batch topic distributions will be returned.

You can get your list of batch topic distributions directly in your browser using your own username and API key with the following links. You can also paginate, filter, and order your batch topic distributions.

Evaluations Last Updated: Monday, 2017-10-30 10:31 An evaluation provides an easy way to measure the performance of a predictive model.

The type of an evaluation can vary. It can be timeseries type if it is created using a time series. The performance measures provided by BigML will vary depending on the type of evaluation. You can also list all of your evaluations.

All the fields in the dataset Specifies the fields in the dataset to be considered to create the evaluation.This offer applies to the FIRST single bet placed by a customer on each bet365 Feature Race. All Accounts: RaceBets Advent Calendar - Countdown to Christmas and get a free treat from RaceBets each day.

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DUCK FEET last start winner at Ascot and could come on strong to threaten, among the main chances. BROTHER'S KEEPER last start winner at Geraldton and has the speed to overcome drawing the widest barrier, among the chances. PRYING TOM has the speed to overcome a very wide draw and two from three wins have been in the dry, don't dismiss. RICH RED unwanted by the market but right up there last start at Ascot when first up and generally strong second-up, place claims.

Young Gina (15) 9. Rare Coin (1) 14. Remunerator (10) BLACKLINE first-up after 26 week spell and placed in both lead-up trials, perfectly placed. YOUNG GINA let-up and chased well to fall just short last start at Bunbury, each-way claims. RARE COIN resumes from a 23 week spell and placed when trialling at Lark Hill, place only. The Gospel Sin (5) 5.

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Paris Texas (7) 4. Kimbo's Girl (8) 1. Majestic Man (1) Looks a toss up between the top two selections. THE GOSPEL SIN a winner at first outing this prep and rates highly with Shaun Mc Gruddy aboard, marginal top pick. PARIS TEXAS back after 24 week break and placed in last trial at Lark Hill, should be thereabouts.

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Convenience stores will hit their peak. He's right: It's common for drugstores like Walgreens, CVS and Duane Reade to have locations that stay open 24 hours a day, should you need to make a 2 a. And though we have yet to see vans circling the neighborhood, Bezos fulfilled his own prediction in a similar way. Amazon recently launched its "Instant Pickup" feature, which allows customers to order staple items such as candy or phone chargers from an app and find a nearby location to pick them up within minutes, CNN reports.

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Bezos missed the mark on this one, but he's not far off. Although retail is suffering big time, it's not for lack of trying. As retailers struggle to attract customers, they admit that making the in-store experience more exciting is crucial, but few have cracked how to do so successfully, CNBC reports.

But as retail sales continue to slip, it seems they'll have to keep pushing for a more complete breakthrough. And according to Business Insider, movie theaters aren't doing so well either.Example: 1 combiner optional Specifies the method that should be used to combine predictions in a non-boosted ensemble. For classification ensembles, the combination is made by majority vote.

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The options are: 0: plurality weights each model's prediction as one vote. You can set up both using the threshold argument. If there are less than k models voting class, the most frequent of the remaining categories is chosen, as in a plurality combination after removing the models that were voting for class. The confidence of the prediction is computed as that of a plurality vote, excluding votes for the majority class when it's not selected.

For regression ensembles, the predicted values are averaged. For a logistic regression, input data for all numerical fields except the objective field must be provided.

Example: "my new prediction" private optional Whether you want your prediction to be private or not. This will be 201 upon successful creation of the prediction and 200 afterwards. Make sure that you check the code that comes with the status attribute to make sure that the prediction creation has been completed without errors. The method used to combine predictions from the non-boosted ensemble.

See the available combiners above. However, for logistic regressions, it really means probability, and thus, confidence will be deprecated soon. Note that this property is not available for ensembles with boosted trees and that for models An array of confidence pairs for each category in the objective field. True when the prediction has been created in the development mode.

The number of predictions in the ensemble that failed. The dictionary of input fields' ids and values used as input for the prediction. Specifies the type of strategy that a model or models in an ensemble will follow when a missing value needed to continue with inference in the model is found. Either 0, 1, or 2 to specify respectively whether the prediction is from a single model, an ensemble, or a logistic regression.

The id of the field that it predicts in the model, ensemble, or logistic regression. A string if the task is classification, a number if the task is regression prediction filterable, sortable A dictionary keyed with the objective field to get the prediction output for the model, ensemble, or logistic regression. An array with a prediction object for each model in the non-boosted ensemble.

An array of probability pairs for each category in the objective field. The parameters (k and class) given when a threshold-based combiner is used for the non-boosted ensemble. A list of the confidence (or expected error in the regression non-boosted ensemble) for each prediction candidate. Bad fields are ignored. That is, if you submit a value that is wrong, a prediction is created anyway ignoring the input field with the wrong value.

An ordered array of Predicate Objects in the decision path from the root to the current node or to a final decision if the the next predicate array is empty. Unknown fields are ignored.

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That is, if you submit a field that is wrong, a prediction is created anyway ignoring the wrong input field. An array of field's ids with wrong values submitted to build the model or logistic regression. A status code that reflects the status of the prediction creation.

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Example: "my new centroid" private optional Whether you want your centroid to be private or not. A dictionary describing the centroid. See the Centroid Object definition below. This will be 201 upon successful creation of the centroid and 200 afterwards.

Make sure that you check the code that comes with the status attribute to make sure that the centroid creation has been completed without errors. This is the date and time in which the centroid was created with microsecond precision. Distance will be set to -1 if BigML can't computer a centroid for a point due to a missing numeric value.