Web logs

In  statistics  and, in particular, in the fitting of  linear regression  models, the  elastic net  is a regularized regression method that combines the L1 and L2 penalties of the  lasso  and  ridge  methods.

Lasso  a regularization technique that's useful for feature selection and to prevent over-fitting training data. It works by penalizing the sum of absolute value (L1 norm) of weights found by the  regression.  

Want something that is potentially human comprehensible? Use decision trees or rules. Have a situation where you have lots of memory, but have to learn incrementally and evaluate quickly? Use Nearest Neighbour. Have a clear binary decision in a continuous space? Use SVMs. Have thousands of independent attributes and lots of data? Use Naive Bayes. Have a situation where you know which attributes are correlated with which? Use Bayes nets.

ML algorithms are an evolution over normal algorithms. They make your programs "smarter", by allowing them to automatically learn from the data you provide. You take a randomly selected specimen of mangoes from the market ( training data ), make a table of all the physical characteristics of each mango, like color, size, shape, grown in which part of the country, sold by which vendor, etc ( features ), along with the sweetness, juicyness, ripeness of that mango ( output variables ). You feed this data to the machine learning algorithm ( classification/regressio n ), and it learns a model of the correlation between an average mango's physical characteristics, and its quality.  Next time you go to the market, you measure the characteristics of the mangoes on sale ( test data ), and feed it to the ML algorithm. It will use the model computed earlier to predict which mangoes are sweet, ripe and/or juicy. The algorithm may internally use rules similar to the rules y...

Credit cards with rewards generally target people who spend a lot on credit cards. That high transaction volume is what makes it viable for issuers to provide the rewards. Rewards  are drawn from the in terchange revenue that issuers get from merchants.    To sup port those high transaction volumes, rewards cards generally have a high credit limit. Lower limits do not make economic sense. These cards are generally offered to people with very good credit as a result.    People with no credit history are generally (but not always) considered to be higher risk by issuers, which translates to lower credit limits, which squeezes out rewards propositions. 

While there are generic FICO scores used for all types of lending,  there also are FICO scores for specific types of lending designed for mortgage lending and insurance purposes. Further, there are FICO scores for specific types of lenders, such as credit unions or traditional banks. Adding to that, there are FICO credit scores created for specific lenders, called custom scores. FICO is a good company, and it produces good credit scores. But it also has competitors, including Scorex, a credit scoring company owned by Experian, that also produce credit scores in much the same way. There are also lenders that produce their own credit scoring systems

The way to solve this is to save the customer’s card number, verify  that it’s legitimate, and then charge all the customers once the deal’s  critical mass has been achieved. However, storing card numbers is a pain because if the card numbers  are stolen from your servers you can face fines which can easily exceed  $millions of dollars for a large breach.  Also, to get a a merchant account with a credit card processor you will need to sign a document telling them that you are PCI compliant.  Being PCI compliant ( http://feefighters.com/b log/easy... ) is pretty easy if you don’t store card numbers, but if you do wish to store card numbers it requires security audits. You can get most of the benefits of storing card numbers locally without worrying too much about PCI by using tokenization ( http://feefighters.com/b log/cred... ). You also have 2 options with respect to verifying the card number before storing it as a token. Do a quick algorithmic check us...