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Utility and Hobby Knife Blades 81 items found. Blade Storage No 1.Once you delete an anomaly detector, it is permanently deleted. If you try to delete an anomaly detector a second time, or an anomaly detector that does not exist, you will receive a "404 not found" response.
However, if you try to delete an anomaly detector that is being used at the moment, then BigML. To list all the anomaly detectors, you can use the anomaly base URL. By default, only the 20 most recent anomaly detectors will be returned. You can get your list of anomaly detectors directly in your browser using your own username and API key with the following links. You can also paginate, filter, and order your anomaly detectors.
Associations Last Updated: Monday, 2017-10-30 10:31 Association Discovery is a method to find out relations among values in high-dimensional datasets.
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It is commonly used for market basket analysis. For example, finding customer shopping patterns across large transactional datasets like customers who buy hamburgers and ketchup also consume bread, can help businesses to make better decisions on promotions and product placements.
Association Discovery can also be used for other purposes such as early detection of failures or incidents, intrusion detection, web mining, or biotechnology. Note that traditionally association discovery look for co-occurrence and do not consider the order in which an item appear within an itemset.
Associations can handle categorical, text and numeric fields as input fields: You can create an association selecting which fields from your dataset you want to use. You can also list all of your associations. This can be used to change the names of the fields in the association with respect to the original names in the dataset or to tell BigML that certain fields should be preferred.
All the fields in the dataset Specifies the fields to be considered to create the association. A value less than 1 represents the percentage of the support, and will be multiplied by the total number of instances and rounded up. Example: true name optional String,default is dataset's name The name you want to give to the new association.
Each must contain, at least the field, and both operator and value. See the description below the table for more details. Example: "lift" seed optional String A string to be hashed to generate deterministic samples.
The individual predicates within the array are OR'd together to produce the final predicate. The above examples in the arguments table specifies that the right-hand side of all discovered rules must be either the item corresponding to species is Iris-setosa and petal width within the interval (1. When a predicate for a numeric field is given, the field will be discretized along bin edges specified by the predicate.
With the above example, the field petal width will be discretized into three bins, corresponding to the values 2. If a predicate is given without an operator or value, then any item pertaining to this field is accepted into the RHS. Discretization is used to transform numeric input fields to categoricals before further processing. You can also use curl to customize a new association. Once an association has been successfully created it will have the following properties.
Creating an association is a process that can take just a few seconds or a few days depending on the size of the dataset used as input and on the workload of BigML's systems. The association goes through a number of states until its fully completed. Through the status field in the association you can determine when the association has been fully processed and ready to be used to create predictions. Thus when retrieving an association, it's possible to specify that only a subset of fields be retrieved, by using any combination of the following parameters in the query string (unrecognized parameters are ignored): Fields Filter Parameters Parameter TypeDescription fields optional Comma-separated list A comma-separated list of field IDs to retrieve.
To update an association, you need to PUT an object containing the fields that you want to update to the association' s base URL. Once you delete an association, it is permanently deleted.Hard But Fair (6) 2. Dal Kilchoan (7) 8. HARD BUT FAIR and DAL KILCHOAN look to be the only possible dangers and WALK WITH KINGS might get 4th by default.
Majic Hazel (2) 1. Lincoln's Gal (12) 3. Swiss Precision (4) 9. Cookie Time (9) The top 3 on the race list look to be the only chances among those that have experience.
Leaning towards MAJIC HAZEL, who was impressive on debut, ahead of LINCOLN'S GAL and SWISS PRECISION. COOKIE TIME only needs to have any ability to be a factor here. Wandering Eye (6) 1.
High Distinction (3) 4. Pakapunch (7) Small field with only 4 realistic chances. WANDERING EYE has gone well in all 3 starts and should have appreciated the freshen up for this. PAKAPUNCH looks to be the only other chance. Hunter Villain (2) 8. Miss Oahu (10) Scratched 9. Petite Midas (3) 6. Windsor (9) HUNTER VILLAIN did enough on debut to warrant strong consideration here. MISS OAHU and PETITE MIDAS are showing improvement and newcomer WINDSOR is the big watch after some solid trial form.
Hand It To Jonesy (2) 7. Helvetica (1) Scratched 3. Rock Rulz (6) Scratched 8. Thats Amore (5) HAND IT TO JONESY has slooked solid in 2 starts so far and is just crying out for this trip.
Looks well suited and a clear top pick. HELVETICA continues to race well and deserves respect, while ROICK RULZ and THATS AMORE appear to be the best of the rest. En Suite (11) Scratched 5.
Royal Ruby (9) 8. Chic (3) TAUTU looked the part at Awapuni and appears to have scope for further improvement. EN SUITE and ROYAL RUBY deserve to be respected and CHIC is inconsistent but a serious chance if she brings her best. Star Of Greenbeel (3) 2. Kamanda Lincoln (2) 5. High Quality (6) Small but open affair with 5 of the 6 appearing to have winning chances.
STAR OF GREENBEEL looks well placed over the distance and will run it out better than most if not all of them. KAMANDA LINCOLN was very poor last start but he's better than that.Sharon Addis, United Kingdom Iceland Full Circle - Winter, March 2017 Your company is terrific.Offerte battat
Sandra Elsom, United Kingdom Northern Lights Circle Tour, February 2017 I was just so impressed with everything.
Amanda Risner, United States Iceland Winter World, January 2017 Thor was excellent. Utterly perfect Naomi Lane, United Kingdom Icehotel Winter Adventure, December 2016 The overall experience exceeded our expectations and was a truly wonderful experience. Mary Dawson, United States A New Year's to Remember in Iceland, December 2016 I cannot express enough how much we loved every aspect of this trip.
Wendy, United States Golden Circle and South Coast - Winter, December 2016 Thanks so much. We got to have exactly the vacation we wanted, and it was just wonderful. Christina, United States Icelandic Winter Highlights, October 2016 The tour exceeded my expectations. It was an unforgettable experience to an extraordinarily beautiful country. The tour guide was sincere, knowledgeable and kind. He being a native to Iceland made the experience even more authentic.
The group size was perfect, enabling the guide to personalize the tour even more. It was good to have some personal time in Reykjavik to explore on my own. I felt it a very walkable city, and I felt completely safe exploring on my own. The locals were always friendly, and willing to help me find my way when I got a little turned around. I've wanted to visit Iceland four about 12 years, and realizing a dream through this tour was more than I could have asked for.
Thank you - I'll be returning to Iceland. Sandy, United States Iceland Winter World, October 2016 Outstanding Tour and the people were wonderful. Hilmar did an outstanding job long distance and this tour was more than I had ever anticipated. What a beautiful place. Thank you for your dedicated help and follow through.We thought the trip was planned beautifully and the timing was perfect.
The excursions were perfectly arranged and something we'll never forget. Helga always answered any questions I had in a timely and thorough manner. We had originally contacted a few travel services in Iceland and you were the best about getting back to us and providing information.
We absolutely LOVED ICELAND and a lot of it had to do with how well this trip was planned for us. The only thing I might do differently, is to have spent two days at the Skaftafell National Park. We felt very comfortable and well taken care of all along the way. We made incredible memories with our nine year old son that we will cherish for a lifetime.
Our trip was wonderful.Olx car bihar patna
The travel documents and communication from Nordic Visitor made this a stress-free, easy trip. There were no problems with the bookings, reservations, rental car, or anything else. The itinerary and map were easy to use. Overall, I couldn't be more happy with Nordic Visitor. Even having traveled a lot this trip exceeded my expectations.Tcs pf trust
Having a planned self drive gave us freedom and structure at the same time. Kolbrun was terrific and the information given excellent. The scenery was magnificent - snow capped mountains, breathtaking waterfalls and fjords extending hundreds of miles inland.
A very enjoyable and memorable trip. Kolbrun did an excellent job of planning and understanding what kind of accommodations we needed.
The hotels were well placed near train stations and the information sent was correct and useful. She responded quickly to a mistake in the dates and made adjustments with an excellent attitude and accommodation. We found Norway to be very beautiful and the people were so friendly and welcoming. Definitely one of the best holidays we have had.
A very interesting and well planned tour with overnight stops on good accommodaton. The areas marked on the map by the agent indicated all the interesting places to see on our tour, all of which were within very easy reach of the main road. An odeal holiday for anyone with any mobility problems. We booked a combination trip to Stockholm, Copenhagen and Oslo with Nordic Visitor and we had a wonderful holiday. We cannot speak highly enough of the people we dealt with at Nordic Visitor and are now recommending them to everyone we meet.
Their levels of professionalism and personal attention went way beyond our expectations and we will use them again. If going on this trip we recommend upgrading to first class rail travel and the commodore cabin on the ferry as these made the travel experience between cities very enjoyable.If you do not specify a range of instances, the complete set of instances in the dataset will be used.
If you do not specify any input fields, all the preferred input fields in the dataset will be included, and if you do not specify an objective field, the last field in your dataset will be considered the objective field. Note that when gradient boosting option is applied to classification models, the actual number of models created will be a product of the number of classes (categories) and the iterations. For example, if you set boosting iterations to 12 and the number of classes is 3, then the number of models created will be 36 or less depending on whether an early stopping strategy is used or not.
Individual trees in the boosted trees differ from trees in bagged or random forest ensembles. Primarily the difference is that boosted trees do not try to predict the objective field directly.
Instead, they try to fit a gradient (correcting for mistakes made in previous iterations), and this will be stored under a new field, named gradient. This means the predictions from boosted trees cannot be combined with using the regular ensemble combiners. Instead, boosted trees use their own combiner that relies on a few new parameters included with individual boosted trees.
These new parameters will be contained in the boosting attribute in each boosted tree, which may contain the following properties. These are sums of the first and second order gradients, and are needed for generating predictions when encountering missing data and using the proportional strategy.
For regression problems, a prediction is generated by finding the prediction from each individual tree and doing a weighted sum using each tree's weight.
Once an ensemble has been successfully created it will have the following properties. Creating a ensemble is a process that can take just a few seconds or a few days depending on the size of the dataset used as input, the number of models, and on the workload of BigML's systems.
The ensemble goes through a number of states until its fully completed. Through the status field in the ensemble you can determine when the ensemble has been fully processed and ready to be used to create predictions. Once you delete an ensemble, it is permanently deleted. If you try to delete an ensemble a second time, or an ensemble that does not exist, you will receive a "404 not found" response.
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However, if you try to delete an ensemble that is being used at the moment, then BigML. To list all the ensembles, you can use the ensemble base URL. By default, only the 20 most recent ensembles will be returned. You can get your list of ensembles directly in your browser using your own username and API key with the following links. You can also paginate, filter, and order your ensembles. Logistic Regressions Last Updated: Monday, 2017-10-30 10:31 A logistic regression is a supervised machine learning method for solving classification problems.
You can create a logistic regression selecting which fields from your dataset you want to use as input fields (or predictors) and which categorical field you want to predict, the objective field. Logistic regression seeks to learn the coefficient values b0, b1, b2. Xk must be numeric values. To adapt this model to all the datatypes that BigML supports, we apply the following transformations to the inputs:BigML.
You can also list all of your logistic regressions. Value is a map between field identifiers and a coding scheme for that field. See the Coding Categorical Fields for more details. If not specified, one numeric variable is created per categorical value, plus one for missing values. This can be used to change the names of the fields in the logistic regression with respect to the original names in the dataset or to tell BigML that certain fields should be preferred.
All the fields in the dataset Specifies the fields to be included as predictors in the logistic regression. If false, these predictors are not created, and rows containing missing numeric values are dropped.
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