Hi @sid100158,

Let us take an example and check the accuracy for both the cases

`data1 <- data.frame(x=seq(from=1,to=100),y=c(seq(from=1,to=30),seq(from=60,to=90),seq(from=1,to=39)))`

The data looks like this

**Point Estimation**

We will use a simple linear model and get the results

The blue line is the predicted value from the model

**Interval Estimation**

```
train$lwr <- as.data.frame(predict(model,train,interval="predict",level=0.8))$lwr
train$upr <- as.data.frame(predict(model,train,interval="predict",level=0.8))$upr
ggplot(data=train,aes(x=x,y=y)) + geom_point(color='RED') + geom_point(aes(x=x,y=lwr),color='BLUE') + geom_point(aes(x=x,y=upr),color='BLUE')
```

We get the following results

The two blue lines determine the lower and upper ranges of the predicted values

If we change the confidence interval from 80% to 95%, there will be a change in the levels

So we can see that an **Interval Estimate** gives us a probabilistic range between which the values can lie. It is always better if you are visually seeing it

If you are feeding the predicted values programmatically into the system you can use the **Point Estimator**

Hope this has helped a bit

Regards,

Anant