Big Data Landscape - Computer Science bibliographies - in Harvard style

In-text: (Brownlee, 2016)

Your Bibliography: Brownlee, J., 2016. What is a Confusion Matrix in Machine Learning. [online] Machine Learning Mastery. Available at: <https://machinelearningmastery.com/confusion-matrix-machine-learning/> [Accessed 7 January 2022].

In-text: (Cross Validated, 2016)

Your Bibliography: Cross Validated, 2016. Overfitting due to a unique identifier among features. [online] Cross Validated. Available at: <https://stats.stackexchange.com/questions/224565/overfitting-due-to-a-unique-identifier-among-features> [Accessed 7 January 2022].

In-text: (GeeksforGeeks, 2020)

Your Bibliography: GeeksforGeeks, 2020. Advantages and Disadvantages of Logistic Regression - GeeksforGeeks. [online] GeeksforGeeks. Available at: <https://www.geeksforgeeks.org/advantages-and-disadvantages-of-logistic-regression/> [Accessed 4 January 2022].

In-text: (Google, n.d.)

Your Bibliography: Google, n.d. Inclusive ML  |  Google Cloud. [online] Google Cloud. Available at: <https://cloud.google.com/inclusive-ml> [Accessed 5 January 2022].

In-text: (Google, n.d.)

Your Bibliography: Google, n.d. The ML.EVALUATE function  |  BigQuery ML  |  Google Cloud. [online] Google Cloud. Available at: <https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-evaluate> [Accessed 6 January 2022].

In-text: (Google, n.d.)

Your Bibliography: Google, n.d. The ML.EXPLAIN_PREDICT function  |  BigQuery ML  |  Google Cloud. [online] Google Cloud. Available at: <https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-explain-predict> [Accessed 3 January 2022].

In-text: (Kurama, 2019)

Your Bibliography: Kurama, V., 2019. Regression in Machine Learning: What it is and Examples of Different Models. [online] Built In. Available at: <https://builtin.com/data-science/regression-machine-learning> [Accessed 4 January 2022].

In-text: (Minitab, 2013)

Your Bibliography: Minitab, 2013. How to Interpret Regression Analysis Results: P-values and Coefficients. [online] Blog.minitab.com. Available at: <https://blog.minitab.com/en/adventures-in-statistics-2/how-to-interpret-regression-analysis-results-p-values-and-coefficients> [Accessed 8 January 2022].

In-text: (Molnar, 2021)

Your Bibliography: Molnar, C., 2021. 5.1 Linear Regression | Interpretable Machine Learning. [online] Christophm.github.io. Available at: <https://christophm.github.io/interpretable-ml-book/limo.html> [Accessed 4 January 2022].

In-text: (Qwiklabs, n.d.)

Your Bibliography: Qwiklabs, n.d. Predict Visitor Purchases with a Classification Model in BQML | Qwiklabs. [online] Qwiklabs. Available at: <https://www.qwiklabs.com/focuses/1794?parent=catalog> [Accessed 31 December 2021].

In-text: (Singh, 2020)

Your Bibliography: Singh, N., 2020. Advantages and Disadvantages of Linear Regression. [online] OpenGenus IQ: Computing Expertise & Legacy. Available at: <https://iq.opengenus.org/advantages-and-disadvantages-of-linear-regression/> [Accessed 4 January 2022].

In-text: (Tutorialspoint.com, n.d.)

Your Bibliography: Tutorialspoint.com, n.d. Machine Learning - Logistic Regression. [online] Tutorialspoint.com. Available at: <https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_classification_algorithms_logistic_regression.htm> [Accessed 1 January 2022].

In-text: (Willmott and Matsuura, 2005)

Your Bibliography: Willmott, C. and Matsuura, K., 2005. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, [online] 30(1), pp.79-82. Available at: <https://www.int-res.com/abstracts/cr/v30/n1/p79-82> [Accessed 2 January 2022].