Answer:
Ottoman Empire came into World war I as one of the Central powers
The marketing mix components is mom most concerned is Place.
<u>Explanation:</u>
In order to get response from the targeted market every business is concerned in choosing the marketing mix components. These plays a very important role for the business success. These are certain variables that are to be considered by the business operators to gain some demand for the product they are going to produce.
There are 4 P's that are considered as the components of marketing mix. They are Place, Product, price and Promotion. People, politics,positioning and packaging are also some of the P's that can be included in the components of marketing mix. Since, the value that is given to the place by Mom, Place is the marketing mix components for which the value is given and mostly concerned with.
<span>The correct answer is confederate. In a Confederate government, a collection of states will be united under one government, but will still function independently of one another. On larger issues such as defense or internal trade, however, the states will work together to come to a solution.</span><span />
<span>The strange situation procedure of Mary Ainsworth, is a procedure in which researchers observes an infant in a different room with or without the presence caretaker, and stranger or another child. They want to observe the attachments styles the child would portray.</span>
.The simple split partitions the data into two mutually exclusive subsets called a training set and a test set . It is common to designate two-thirds of the data as the training set and the remaining one-third as the test set.
How do you measure accuracy of classification models?
There are many ways for measuring classification performance. Accuracy, confusion matrix, log-loss, and AUC-ROC are some of the most popular metrics. Precision-recall is a widely used metrics for classification problems
Classification accuracy:
is a metric that summarizes the performance of a classification model as the number of correct predictions divided by the total number of predictions. It is easy to calculate and intuitive to understand, making it the most common metric used for evaluating classifier models.
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