Machine learners commonly used in predictive analytics all have at least some roots in statistics.

Some, such as regression, PCA and K-means, have been considered statistical methods since they were first developed.

Newer methods such as random forests and XGBoost will often outperform methods such as linear and binary logistic regression.

However, this depends on how the regression methods are used - statisticians often criticize data scientists for using methods like these incorrectly or for not using newer variations better suited to a particular data set.

In general, it takes more work to get statistical methods right, but this effort may pay off in the long term. Statistical methods can also be automated once the general parameters are known.

When it comes to helping us understand our data and the process(es) which generated them, as well as for what if simulations, statistical methods are better suited.

I think there is also confusion about tasks. Data are not data, and there are some tasks for which newer approaches such as Deep Learning (DL) are better suited. NLP and speech recognition are two examples.

However, because methods such as DL are better suited to "exotic" tasks than statistics, this does not mean they are better for any task (e.g., survey analytics).

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