What is the correct arrangement of the process of machine learning?

The arrangement of the processes in machine learning outlined by the correct choice reflects the logical flow of developing a machine learning model.

To start, data collection is the initial step where raw data is gathered from various sources, which forms the foundation for any machine learning project. Following that, data cleansing is necessary to process and clean the collected data by removing errors, handling missing values, and eliminating duplicates, thus ensuring the quality of the dataset.

Next, feature extraction occurs, which involves selecting and constructing relevant features that the model will use to make predictions. This is a critical step because the quality and relevance of the features directly impact the model's performance.

After preparing the data and selecting features, model training takes place. This phase involves using the prepared data to train a machine learning model, which learns to recognize patterns or make predictions based on the inputs.

Once the model is trained, it is important to evaluate its performance using a portion of the data that was not used during the training period. Model evaluation assesses metrics like accuracy and precision to determine how well the model performs.

Finally, model deployment involves implementing the trained and evaluated model into a production environment where it can make predictions or decisions based on new data.

Thus, the correct sequence—data collection,