The majority of automated machine learning (AutoML) solutions are developed in Python, however a large percentage of data scientists are associated with the R language. Unfortunately, there are limited R solutions available. Moreover high entry level means they are not accessible to everyone, due to required knowledge about machine learning (ML). To fill this gap, we present the forester package, which offers ease of use regardless of the user's proficiency in the area of machine learning. The forester is an open-source AutoML package implemented in R designed for training high-quality tree-based models on tabular data. It fully supports binary and multiclass classification, regression, and partially survival analysis tasks. With just a few functions, the user is capable of detecting issues regarding the data quality, preparing the preprocessing pipeline, training and tuning tree-based models, evaluating the results, and creating the report for further analysis.

We discussed each step in the previous part using the graph below. Now we will try to explain how our package exactly works and what happens between the first and the last step of the process. The basic scheme of functions of our package is presented on the graph below. The only thing user has to do to create a model is to run one function. Then the data preprocessing is performed.

Drought, heat, and pest infestation: Climate change is threatening forests in Germany and represents a big challenge in forest management. A joint project of Karlsruhe Institute of Technology (KIT) and EDI GmbH, a spinoff of KIT, now provides support. Together with partners in the forestry sector, they are developing the EDE 4.0 assistance system. Based on artificial intelligence (AI), it helps foresters preserve and sustainably manage forests. Climate change also affects forests in Germany.

Forests in Germany and around the globe are suffering from climate change and the resulting drought, heat and pest infestation. Germany is currently experiencing the largest forest die-off since the 1980s, and in Baden-Württemberg alone, 43 percent of all forest areas have already been damaged. All of this poses major challenges for foresters and those responsible for forest management. They will now get help from a joint project of the Karlsruhe Institute of Technology (KIT); one of its spin-offs, EDI GmbH; the South German Climate Office; and the KIT Institute of Geography and Geoecology. According to the scientists, research findings could help to counteract the effects of climate change and make forest ecosystems more resilient through targeted measures: "Mixed forests with hornbeam, maple, or wild cherry can cope better with the new conditions than spruce forests, for example – although they are less productive. In plantations, soil conditions also play an important role," says climate researcher Dr. Joachim Fallmann from the South German Climate Office at KIT. "Forest management must now react while making complex trade-offs in the process."

The Tin Woodman first appeared in Frank Baum's The Wonderful Wizard of Oz 120 years ago. Now real robot foresters are making their debut, planting trees rather than cutting them down. The robotic foresters are the work of robot makers Milrem in partnership with the University of Tartu, both based in Estonia. Two versions are under development based on the company's range of driverless ground vehicles. One type is a planter, the other a brush cutter, and both are autonomous.

AI startup Trefos is helping foresters see the wood for the trees. Using custom lidar and camera-mounted drones, the Philadelphia-based company collects data for high-resolution, 3D forest maps. These metrics allow government agencies and the forestry industry to estimate the volume of timber and biomass in an area of forest, as well as the amount of carbon stored in the trees. With this unprecedented detail, foresters can make more informed decisions when, for example, evaluating the need for controlled burns to clear biomass and reduce the risk of wildfires. "Forests are often very dense, with a very repetitive layout," said Steven Chen, founder and CEO of the startup, a member of the NVIDIA Inception program, which supports startups from product development to deployment. "We can use deep learning algorithms to detect trees, isolate them from the surrounding branches and vines, and use those as landmarks."

Subaru isn't exactly known for developing emerging technologies for its vehicles, so we'll bet you'd never expect the automaker to equip the 2019 Forester with facial recognition technology. But that's exactly what it did -- Subaru has announced at the ongoing New York International Auto Show that it developed a feature for the vehicle that uses facial recognition to detect driver fatigue and distraction. "DriverFocus" comes as a standard feature for the most expensive Touring version of the vehicle, though it's unclear if you can pay extra to have it installed on another model. The feature runs on top of Subaru's new driver assist system called EyeSight, which (unlike DriverFocus) will come pre-installed on all Forester models. It's not a hands-free driving technology, but it covers basic driver assist offerings, such as adaptive cruise control, lane departure warning and lane assist, as well as pre-collision braking. By being able to detect whether a driver is sleepy or tired, the system can be on the alert and activate EyeSight's functions when needed.