Machine learning teaches computers to behave like humans by giving them historical data and predictions for the future. This article examines intriguing machine learning techniques such as query-level features, randomized weighted majority, and rule-based machine learning.

Query-level feature

A query-level feature (QLF) is a ranking feature used in machine-learning algorithms. These features concern how users reformulate inquiries without regard for the terms used.

Query similarity:

Because each reformed query may be syntactically similar to its initial question when dealing with the same information demand. The similarity between queries may help decide if one query is an appropriate reformulation of another.

Query length:

We compute the average number of terms in a query for a session based on the same search intent. The higher the ratio value, the longer the recent question was than the average length of previous searches.

Query frequency:

Search logs can provide helpful information, such as query co-occurrence, which aids query recommendation.

Randomized weighted majority

The random weighted majority algorithm is a machine learning theory algorithm. It enhances the weighted majority algorithm's error limit. For example, imagine if every morning before the stock market opens, each of our "experts" predicts whether the market will rise or fall. The objective is to aggregate this collection of forecasts into a single forecast, which we will then use to determine whether to buy or sell for the day. The RWMA allows us to combine these variables so that our prediction record will be comparable to that of the best expert in hindsight.

Multiple methods can be combined using the Randomized Weighted Majority Algorithm, in which RWMA is projected to perform nearly as well as the best of the original algorithms. In addition, the Randomized Weighted Majority Algorithm can be utilised in circumstances in which experts make recommendations that we cannot merge (or cannot be combined easily). For instance, RWMA applies to repetitive game-playing and the online shortest path problem. Each expert recommends a unique route to work in the online shortest path problem.

Rule-based machine learning

Rule-based machine learning (RBML) is used in computer science to refer to any machine learning technique that identifies, learns, or evolves rules to store, manipulate, or apply. Rule-based machine learning is characterised by identifying and using relational practices collectively representing the system's captured knowledge. It contrasts conventional machine learning techniques, which typically establish a single model that can be applied to any occurrence to create a prediction.

Rule-based machine learning techniques consist of learning classifier systems, association rule learning, artificial immune systems, and any other technique that relies on a collection of rules containing contextual knowledge. Although rule-based machine learning is essentially a rule-based system, it is unique from traditional rule-based systems, which are often hand-crafted, and other rule-based decision-makers. It is because rule-based machine learning uses a learning algorithm to identify good rules automatically, rather than requiring a human to manually design and curate a rule set using prior domain expertise.