Photo: Anete Lusina

What's an example of a prediction?

Prediction: If marigolds deter asparagus beetles, and we grow asparagus next to marigolds, then we should find fewer asparagus beetles when asparagus plants are planted with marigolds.

trentu.ca - Understanding Hypotheses and Predictions - Academic Skills

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Make Your Bookie Cry And Pull The Hair By Generating $23,187

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Hypotheses and predictions are different components of the scientific method. The scientific method is a systematic process that helps minimize bias in research and begins by developing good research questions.

Research Questions

Descriptive research questions are based on observations made in previous research or in passing. This type of research question often quantifies these observations. For example, while out bird watching, you notice that a certain species of sparrow made all its nests with the same material: grasses. A descriptive research question would be “On average, how much grass is used to build sparrow nests?” Descriptive research questions lead to causal questions. This type of research question seeks to understand why we observe certain trends or patterns. If we return to our observation about sparrow nests, a causal question would be “Why are the nests of sparrows made with grasses rather than twigs?”

Hypothesis

In simple terms, a hypothesis is the answer to your causal question. A hypothesis should be based on a strong rationale that is usually supported by background research. From the question about sparrow nests, you might hypothesize, “Sparrows use grasses in their nests rather than twigs because grasses are the more abundant material in their habitat.” This abundance hypothesis might be supported by your prior knowledge about the availability of nest building materials (i.e. grasses are more abundant than twigs).

Prediction

On the other hand, a prediction is the outcome you would observe if your hypothesis were correct. Predictions are often written in the form of “if, and, then” statements, as in, “if my hypothesis is true, and I were to do this test, then this is what I will observe.” Following our sparrow example, you could predict that, “If sparrows use grass because it is more abundant, and I compare areas that have more twigs than grasses available, then, in those areas, nests should be made out of twigs.” A more refined prediction might alter the wording so as not to repeat the hypothesis verbatim: “If sparrows choose nesting materials based on their abundance, then when twigs are more abundant, sparrows will use those in their nests.” As you can see, the terms hypothesis and prediction are different and distinct even though, sometimes, they are incorrectly used interchangeably.

Example

Let us take a look at another example:

Causal Question: Why are there fewer asparagus beetles when asparagus is grown next to marigolds?

Hypothesis: Marigolds deter asparagus beetles.

Prediction: If marigolds deter asparagus beetles, and we grow asparagus next to marigolds, then we should find fewer asparagus beetles when asparagus plants are planted with marigolds.

A final note

It is exciting when the outcome of your study or experiment supports your hypothesis. However, it can be equally exciting if this does not happen. There are many reasons why you can have an unexpected result, and you need to think why this occurred. Maybe you had a potential problem with your methods, but on the flip side, maybe you have just discovered a new line of evidence that can be used to develop another experiment or study.

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How do you know if an algorithm is accurate?

For Classification Model: Precision = TP/(TP+FP) Sensitivity(recall)=TP/(TP+FN) Specificity=TN/(TN+FP) Accuracy=(TP+TN)/(TP+TN+FP+FN)

medium.com - Calculating Accuracy of an ML Model. | by Abhigyan | Analytics Vidhya

Calculating Accuracy of an ML Model.

Before moving on with Accuracy Metrics, Let’s know about the types of learning involved in ML. Supervised Learning — It is a learning technique in which the dependent variable is mentioned and we have to predict according to it.Supervised learning are of two types: Regression : This type has it’s dependent variable as an continous feature. Example: House price data.(In which we have to predict House prices). Classification : This type has it’s dependent variable in a binary or multiclass feature. Example: Titanic data.[In which we have to predict whether a person survived(1) or not(0)] Un-Supervised Learning — It is learning technique in which the dependent variable is not mentioned.This learning technique is usually used in real-life problems with the e-commerce or finance,etc, companies to target the most valued customers. Semi-Supervised Learning — As the name suggests,this technique includes both Supervised and Un-Supervised techniques.The data contains both some amount of labelled data but mostly un-labelled.To understand more about semi-supervised learning check out this link. Now that we know about ML,Let’s dive in to metrics used to calculate accuracy in Supervised Learning.

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