Steps in Machine Learning

Which of the following are sources of data in a machine learning project?

• Company internal databases

• Collecting data from website for eg. product reviews

• Social media

• All of the above

How does quantity and quality of the training data impact the model?

• Performance and accuracy improves with more data.

• Noisy, irrelevant or incorrect data do not affect the model

• Imbalanced data can lead to biased models that perform well on the majority class but poorly on minority classes.

• Feature selection (the process of selecting the input features that will be used to train the machine learning model) have no effect on the model.

How can we prepare our data for training?

• Randomize the order of the data so it does not affect how the model learns.

• Removing outliers and handling missing values

• Select an appropriate algorithm

• Identifying data imbalances.

• Splitting the data into training data and evaluation data.

Which of the following is most important when choosing a model for a machine learning project?

• The programming language used to implement the model.

• The type of data and the problem you are trying to solve.

• The color scheme of the data visualization.

• The font size of the code comments.

What is the purpose of the model training step in machine learning?

• To enable the model to learn patterns and relationships from the provided data so that it can make predictions or decisions when presented with new, unseen data.

• Generating new data samples by extrapolating from existing data to enhance the diversity of the dataset and increase the model's robustness to variations.

• Enhancing the interpretability of the model's predictions by providing explanations for each decision made

• Increasing the computational complexity of the model to improve its accuracy on the training data

In the model evaluation step, we?

• Clean and preprocess the dataset to ensure its quality, remove noise, handle missing values, and transform features into a suitable format for modeling.

• Train the machine learning model by fitting it to the preprocessed dataset to learn patterns and relationships.

• • Use the dataset we put aside to test the model with the unseen data comparing the predicted outputs with the actual outputs.

• Select the appropriate machine learning algorithm based on the problem type, data characteristics, and performance requirements

What is a common ratio for splitting data into training and testing sets in machine learning?

• Training set: 50%, Testing set: 50%

• Training set: 70-80%, Testing set: 20-30%

• Training set: 90%, Testing set: 10%

• Training set: 40-50%, Testing set: 50-60%

What is the primary purpose of parameter tuning in machine learning?

• Selecting the appropriate machine learning algorithm.

• Finding the best values for hyperparameters to optimize model performance.

• Splitting the data into training and testing subsets.

• Cleaning and preprocessing the dataset.

What is the primary purpose of the prediction step in machine learning?

• Assessing the quality of the dataset.

• Training the model on the entire dataset.

• Making predictions on new, unseen data.

• Visualizing the relationships between variables.

What is the primary goal of machine learning?

• To automate repetitive tasks.

• To analyze historical data.

• To enable computers to learn from data and make predictions or decisions.

• To develop advanced algorithms.