A decision tree is a flowchart-like tree structure where each node is used to denote feature of the dataset, each branch is used to denote a decision, and each leaf node is used to denote the outcome. The topmost node in a decision tree is known as the root node.
It learns to partition on the basis of the feature value. It partitions the tree in a recursive manner, also call recursive partitioning. This flowchart-like structure helps in decision making. That is why decision trees are easy to understand and interpret. The major problem with decision trees is overfitting, which is why they will perform well on the validation dataset but will have poor accuracy on the test dataset. To overcome this issue posed by decision trees, data scientists came up with ensemble learning.
To understand how exactly decision trees divide the data recursively, you can go through this article. Ensemble learning, in general, is a model that makes predictions based on a number of different models. By a combining a number of different models, an ensemble learning tends to be more flexible less bias and less data sensitive less variance. The two most popular ensemble learning methods are bagging and boosting.
Bagging : Training a bunch of models in parallel way. Each model learns from a random subset of the data. The application of bagging is found in Random Forests. Random forests are a parallel combination of decision trees.
Each tree is trained on random subset of the same data and the results from all trees are averaged to find the classification. The application of boosting is found in Gradient Boosting Decision Trees , about which we are going to discuss in more detail.
Boosting works on the principle of improving mistakes of the previous learner through the next learner. In boosting, weak learner are used which perform only slightly better than a random chance. Boosting focuses on sequentially adding up these weak learners and filtering out the observations that a learner gets correct at every step.
Basically the stress is on developing new weak learners to handle the remaining difficult observations at each step. One of the very first boosting algorithms developed was Adaboost. Gradient boosting improvised upon some of the features of Adaboost to create a stronger and more efficient algorithm.
Adaboost used decision stumps as weak learners. Decision stumps are decision trees with only a single split. It also attached weights to observations, adding more weight to difficult to classify instances and less weight to easy to classify instances.
The aim was to put stress on the difficult to classify instances for every new weak learner. Further, the final result was average of weighted outputs from all individual learners. The weights associated with outputs were proprotional to their accuracy. Instead of using the weighted average of individual outputs as the final outputs, it uses a loss function to minimize loss and converge upon a final output value.
The loss function optimization is done using gradient descent, and hence the name gradient boosting. Further, gradient boosting uses short, less-complex decision trees instead of decision stumps. In gradient boosting decision trees, we combine many weak learners to come up with one strong learner.
The weak learners here are the individual decision trees. All the trees are conncted in series and each tree tries to minimise the error of the previous tree. Due to this sequential connection, boosting algorithms are usually slow to learn, but also highly accurate. In statistical learning, models that learn slowly perform better. Boost multiplies the amount you earn for each trip. Since both Boost and Surge multiply the amount you earn for each trip, you will always earn the higher of the two.
In fact, if you click on the three dots icon you usually click to edit a Facebook post, you will see the option to edit the post is simply not there. In some cases, it might be easier to simply delete your post and start over.
However, if you have already got likes, comments or shares of your boosted post, this method allows you to retain that engagement. If you work with influencers or other brand advocates to create branded content, you might want to boost posts they create in which they mention and tag your brand. Source: Facebook. Click View Results from any boosted post to get detailed metrics about how the post is performing.
Over time, you can refine your boost post strategy to get a better return on investment. Facebook research shows that ads developed through testing cost less over time. People learning about your brand for the first time may be more likely to trust your content if they see plenty of existing engagement from others. You can find out which organic posts are performing best and are therefore worthy of a boost by checking analytics on the Insights tab for your Facebook business page.
You can also check for high-performing content in Hootsuite Analytics. You can also choose an Instagram post to boost to Facebook. Boost your Facebook posts and manage your other social media channels in the same easy-to-use dashboard with Hootsuite.
Get Started. Schedule, engage, analyze, perform. In this social media advertising guide, learn how to use various types of social media ads to achieve real business results while maximizing your spend. It's not too hard to create successful Instagram ads, as users are already primed to shop on the platform.
We could have SWORN you were someone who wanted to blow your competition out of the water on social media. Our bad. So if budgets are tight, then think carefully about spend on Instagram. It might be that with a little more effort put into hashtagging on Instagram you can create more buzz on this platform that you would get by paying for it.
In order to know if an online advert has been successful you need to measure your success. This is essential. As well as an at-a-glance view of how many people were reached and how many engagements you got for your spend, you can drill into each post individually to examine how well certain posts performed.
If you have multiple audiences that you have been testing out, you will also be able to see if one works better for your brand than another, helping you with learning to understand your audience better. This kind of observation helps you in other areas of your business when putting together your sales messaging.
Once you have figured out the basics on Facebook and Instagram, you might find these articles interesting.
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