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Understanding Classification Models in the Hotel Industry

05 June 2024


By Alexis Ihezue

What is a Classification Model?

In machine learning, a classification model is an algorithm that uses input data to try and accurately predict the labels within that dataset. Labels refer to the desired output or identification produced by the algorithm. Classification models are prevalent in everyday tools such as spam filters in emails. These filters use a classification algorithm to categorize certain emails as spam or not spam, the two categories serving as the ‘labels.’

Types of Classification Models

There’s no one-size-fits-all classification model; there are multiple considerations when determining the type of algorithm that will work best with the data and the desired output. Understanding the type of data being used is essential. Many datasets can have a mix of categorical and numerical data, complete with a range of values associated with each.

Some variables in the dataset may also describe specific features or characteristics that are relevant to other variables within the dataset. Classification models do not have to use data based solely on words or numbers. Images or audio can also be input into classification models. When deciding what model works best, gaining this type of insight will be helpful, especially when considering what labels need to be predicted and what visualizations are needed depending on findings.

Furthermore, regardless of the model chosen, no model will be effective when working with a limited dataset. A large dataset is always best; one with enough entries to represent all the categories soundly is even better. Suppose the dataset is too small or entries for specific groups or categories in the dataset vastly outnumber others. In that case, it may hinder the model’s ability to use the input data to make sound predictions.

Specific classification models work well with small datasets like k-nearest neighbors or decision trees. However, the performance metrics associated with each model may still point to a problem with the size of the dataset. That’s not to say larger datasets have no issues. Storage and memory availability can also be a grave concern.

Other types of classification models to choose from include, but are not limited to, the following:

– Naïve Bayes
– Logistic Regression
– Stochastic Gradient Descent
– K-Nearest Neighbors
– Decision Tree
– Support Vector Machine

Following the output of all classification models, these models generate their respective labels but use different methods. For instance, the k-nearest neighbor is a distance-based algorithm that labels data according to the labels of the data or ‘neighbors’ closest to it, whereas another model that uses an algorithm such as logistic regression is commonly used for binary classification. It will output the probability of belonging to a specific category within the dataset or not, ranging between 0 and 1.

Model Performance Metrics

The final step in the modeling process is evaluating the model’s performance, and several metrics can be utilized.

A confusion matrix is commonly used; it is a table that counts the number of times the model falsely and positively predicts a model. True positives and negatives are when the model correctly predicts that the selected label either matches or does not match the label being compared to. False positives and false negatives are when the model incorrectly determines a label matches the label it is being compared to or incorrectly determines a label does not match when it does. The number of true positives and negatives and their counterparts can also generate other evaluation metrics such as accuracy, precision, recall, and the F1 score.

Accuracy measures the proportion of true positives and true negatives out of all the predictions. A model with high accuracy can indicate that a model is performing exceptionally well. However, when the dataset the model is trained on is small, the accuracy score may not be correct.

Precision is a ratio of true positives to all positive predictions (true and false) generated by the model. It examines the accuracy of positive predictions.

Recall is a ratio similar to precision, except it is all true positives to the sum of true positives and false negatives. It determines how well the model can correctly identify positive instances.

A model with both high precision and recall can be hard, but the F1 score, which is the harmonic mean of both, can help strike a balance. It works well with data in which not all the categories are equally represented. The F1 score is between 0 and 1, and the closer it is to 1, the better the performance in terms of both precision and recall.

Relevance to the Hotel Industry

Classification models can be an excellent tool to leverage in the hospitality industry. With the models outlined, algorithms can be implemented to parse through data and determine how customers feel about the products or services they receive and what products or services may need to be improved or can be maximized. These models can also be utilized to create personal recommendations, expand on customer profiles, and determine what attention can be given to specific marketing strategies. That’s why gathering the relevant data and deciding what model works best is essential.

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