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How this simple mistake cost me 15,000 Delta SkyMiles


It’s hard to imagine that a simple error could cost thousands of points or miles, but that’s exactly what happened to me while booking a flight to Cancun for my friends and me.

I’ve always been diligent about managing my rewards since I’ve heard horror stories of people losing large chunks of their balances through one mistake or another. But nobody’s perfect: I put too much trust in my account to auto-update after a points transfer, and it ended up costing me 15,000 miles.

Here’s what happened and how you can avoid making the same mistake.

The transfer: Moving Membership Rewards to Delta SkyMiles

Two of my friends and I decided to go to Cancun this summer, and I volunteered to book us with points and miles.

At the time, I had close to 200,000 Amex Membership Rewards points, so Delta Air Lines was at the top of my list of airlines to consider, since it’s one of Amex’s transfer partners. I knew I could easily book a few tickets to Cancun and still have a ton of points to spare.

Playa Del Carmen Beach, Mayan Riviera Hotels near Cancun, Mexico
Playa Del Carmen Beach, Cancun, Mexico. YINYANG/GETTY IMAGES

One of my friends wasn’t flexible on our travel dates due to her work schedule, so I kept a close eye on Seats.aero to find the best option for us. Delta turned out to be the cheapest option for our flight to Cancun, perfect luck for me.

In total, I’d need to transfer 69,000 Membership Rewards points to Delta SkyMiles to book this flight from Seattle-Tacoma International Airport (SEA) to Cancun International Airport (CUN). So, that’s exactly what I did.

Historically, my transfers have been credited to my account immediately. This time, though, the miles didn’t post right away. I made the transfer on a holiday weekend, and I’d heard transfers can sometimes take a couple of days to post, so I waited.

Big mistake.

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Related: Why you should master a few credit card transfer partners, and (most likely) leave the rest behind

The mistake: Waiting too long to contact Delta support

I waited a few days after my transfer to contact Delta support.

My loyalty account balances have always auto-updated after past transfers, so, at the time, I thought Delta’s system was just taking a long time to process my transfer.

In reality, the transfer had posted right after I made it. When I contacted support, a representative confirmed that my updated balance wasn’t showing on my account due to a technical issue on Delta’s side. The fix to see them was simple: log out and back in.

As soon as I logged out and in, the problem was fixed. My 69,000 SkyMiles were available.

Woman talking on the phone
MIXETTO/GETTY IMAGES

I immediately navigated to Delta’s booking page to snag three award tickets, only to find that the flight I’d chosen had gone up in price in the few days since I last checked.

It wasn’t anything jaw-dropping, but it wasn’t a painless increase, either. In total, I had to transfer an additional 15,000 Membership Rewards points to Delta to cover the additional cost.

I’m fortunate to have a significant Membership Rewards balance, thanks in large part to the welcome offers I earned from my American Express® Gold Card and American Express Platinum Card®.

If those 69,000 points were all I had, I would’ve been in a very tough spot. Transfers are always one-way, so I wouldn’t have had the option to use those points for another airline.

Related: I had no rebooking options after my flight was canceled. Here’s how transferable points got me home

The lesson: Always be proactive about contacting support if your transfer isn’t posting

The mistake itself was easy to avoid. I should’ve contacted support after 24 hours, since my previous transfers always posted instantly. Any unexpected delay in your rewards posting is worth contacting support about, just in case there’s a glitch.

Even if nothing’s wrong, take it from me: It’s better to be safe than sorry.

Man makes a phone call while trying to make an online purchase using a credit card and a laptop
KOSAMTU/GETTY IMAGES

This is especially true if you’re using most or all of your rewards balance for a transfer, or if your travel dates are inflexible. Of course, you could always buy additional points or miles if you need more and don’t have any left to transfer. But that can get costly if you need to buy a substantial number of points and miles.

In the end, the best strategy is to contact support if something feels off. Don’t trust that your account balance will display properly.

Related: Don’t fall for this airline customer service scam

Bottom line

In the end, I was able to book the flight for my friends and me. 15,000 SkyMiles didn’t annihilate my rewards balance, but it took enough of a chunk for me to learn an important lesson.

I spent so much time focusing on big picture things like using the right card for certain purchases and monitoring award trackers that I missed something so simple. If you plan to make a transfer to Delta SkyMiles soon, remember to log out of your account and back in after the transfer completes.

Above all else, don’t hesitate to contact your loyalty program’s support if you’re concerned about how long a transfer is taking. If something feels off, it probably is.

Related: 7 mistakes to avoid when redeeming hotel points



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Feature Selection Techniques In Machine Learning


What Are Selection Techniques

Selection techniques in machine learning help in reducing the noise by taking in only the relevant data after the pre-processing. The techniques have the ability to choose the relevant variables according to the type of user’s problem. In case any data comes up that is not relevant to the requirement, it tends to slow down the efficiency process of the model and also decrease the accuracy. Therefore, it is very important to have appropriate feature selection techniques for the models in order to have better outcomes and accuracy. 

The main idea of working with selection techniques is to manually extract the relevant settings from the parent set to have high-accuracy model structures.

Feature Selection in Machine learning

The techniques are divided into the category of supervised and unsupervised learning. These two categories are further divided into 4 main methods for selecting the features.

Filter Method :

There are statistical ways for selecting the features using the filter method. The features are selected in the pre-processing stage as there is no learning process involved in this. The aim of this approach is to filter out the unrequired and irrelevant features by using matrices and ranking methods. The most important advantage of using the filter method is that it does not overfit the data.

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Wrapper Method :

In this method, a user makes different combinations that are evaluated or compared with a lot of other possible combinations. In this way, the feature selection is done. A subset of features is selected and the algorithm is trained based on the subset. The output of the algorithm then decides if the features will be added or not. This method is further based on 4 types which are:

  • Forward Selection : This process takes in an empty feature set. It keeps adding a feature to each interaction and checks the progress simultaneously as if it is improving or not. This method keeps on iterating unless there comes a feature that does not improve the progress of the model.
  • Backward Elimination : This approach is the complete opposite of the forward selection approach. The process takes in all the features of the algorithm and then keeps removing a feature one by one on each iteration. It checks the progress simultaneously as if it is improving or not. This method keeps on iterating unless there comes a feature that does not improve the progress of the model.
  • Exhaustive Feature Selection : It is the most common approach for feature selection as each feature is set as brute-force. The approach aims to try various combinations of features in order to give the best outcome.
  • Recursive Feature Elimination : This method is based on the greedy approach as its features are selected in a smaller amount. An estimator is made to test every set of features designed and thus we get an outcome of the best features.
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Embedded Method :

This is a great method for feature selection as it has the advantages for both filter and wrapper methods collectively. The processing time in the embedded method is very high just like the filter method, however, they provide more accurate outcomes.

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There are a few techniques involved with embedded methods which are:

  • Regularisation : This aims at regularising the feature selection method simply by adding a penalty if the data gets overfitted in the model. The points shrink to a value of 0 and they are eliminated from the dataset. The types of regularizations are L1, L2, L3, etc. 
  • Random Forest Importance : This technique involves a lot of tree-based approaches to select the features for an algorithm. A number of decision trees are involved in this as the ranking of nodes is performed in all the trees to get the results. After filtering out the irrelevant nodes, a subset of the most relevant nodes creates a final selection of features.
Hybrid Method :

This approach takes in features as small-sized samples. The main idea is to select the features using instance learning. The features that correspond to the instances are selected as they are relevant to the algorithm.

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Feature Selection Models

Supervised Model :

This model is defined as the class of machine learning methodologies where the user can train with the help of continuous and well-labelled data. For instance, the data can be historical data where the user wishes to predict whether a customer will take a loan or not. Supervised algorithms tend to train over the well-structured data after the preprocessing and feature characterization of this labelled data. It is further tested on a completely new data point for the prediction of a loan defaulter. The most popular supervised learning algorithms are the k-nearest neighbour algorithm, linear regression algorithm, logistic regression, decision tree, etc.

This is further divided into 2 categories:

  • Regression: The dealing of output variables is done using regressions as it includes graphs, images, etc. For example to determine age, height, etc. 
  • Classification: it helps in classifying different objects such as yellow, orange, wrong or right, etc.
Unsupervised Model

This model is defined as a class of machine learning methodologies where the tasks are performed using the unlabelled data. Clustering is the most popular use case for unsupervised algorithms. It is defined as the process of grouping similar data points together without manual intervention. The most popular unsupervised learning algorithms are k-means, k-medoids, etc. 

This is further divided into 2 categories:

  • Clustering :This means when the machine requires an inherent group while training the data.
  • Association :This category has a set of rules which helps in the identification of massive data. For example, a list of students who could be interested in artificial intelligence as well as machine learning.
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How To Choose a Feature Selection Model

It is very important for machine learning engineers as well as researchers to understand which feature selection model is most suitable for them. The most data types are known by the engineer, the easier it will be for him to choose properly and wisely. This whole concept is based on 4 main approaches which are:

  • Numerical Input, Numerical Output : There are two methods used in this technique which are Pearson’s correlation coefficient and Spearman’s Rank Coefficient.  The numerals are basically used for the prediction of regression models for continuous numerical such as int, float, etc. 
  • Numerical Input, Categorical Output : There are two methods used in this technique which are the ANOVA correlation coefficient, and Kendall’s rank coefficient. The numerals are basically used for the classification of predictive models for continuous numerical such as int, float, etc. 
  • Categorical Input, Numerical Output : This is a case of the prediction of regression models using input based on categories. The process is the same as numerical input, and categorical output but in a reverse fashion. 
  • Categorical Input, Categorical Output : This is a case of classification of predictive models using both categorical inputs as well as outputs. The main approach affiliated with this method is the Chi-squared method. Moreover, information gain can also be used with this technique.

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Conclusion:

The process of selecting features in machine learning is a vast concept and it involves a lot of research to select the best features. However there is no hard and fast rule for making the selection, it all depends on the type of model and its algorithm and how a machine learning engineer wants to pursue it. Selection techniques in machine learning help in reducing the noise by taking in only the relevant data after the pre-processing. 

In this article, we have talked about various feature selection methods that use certain algorithms for making the best possible outcomes and why we should make this feature selection method. Along with this, we have talked about how we can finalise the best feature selection model to work with.

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