Yes, it is possible that validation accuracy cannot be as high as training accuracy. Any situation in which noise (as opposed to generalizable properties of the feature set) in the training set is more predictive of the target variable within the training set would produce this.

Consider a situation in which a random property of the training sample considered is perfectly predictive, but this is found not to be true of all other examples outside the sample. The predictive power in the training set would be perfect, but outside the training set, less than perfect.

For example, let's consider a case where you have a set of fruit data and you're trying to establish whether a given fruit is an orange or a tangerine. You have 4 features- the circumference of the fruit around the stem-medial axis, the height of the fruit across the stem, a numerical value of the hue of the fruit skin, and the first letter of the amateur baseball team whose home park is closest to the field the fruit was grown in. Let's imagine that by some baffling coincidence, the baseball team letter in the training set was perfectly predictive of whether the fruit would be a tangerine or orange.

We can imagine that this would not hold true across the country or the world, which would produce a situation where the training set accuracy would be perfect, but the validation set accuracy would not be able to approach that using the same methods.

Yes, it is possible to have a situation in which validation accuracy cannot be as high as training accuracy. Any situation in which noise (as opposed to generalizable properties of the feature set) in the training set is more predictive of the target variable within the training set would produce this.

Consider a situation in which a random property of the training sample considered is perfectly predictive, but this is found not to be true of all other examples outside the sample. The predictive power in the training set would be perfect, but outside the training set, less than perfect. This phenomenon is broadly referred to as "overfitting".

For example, let's consider a case where you have a set of fruit data and you're trying to establish whether a given fruit is an orange or a tangerine. You have 4 features- the circumference of the fruit around the stem-medial axis, the height of the fruit across the stem, a numerical value of the hue of the fruit skin, and the first letter of the amateur baseball team whose home park is closest to the field the fruit was grown in. Let's imagine that by some baffling coincidence, the baseball team letter in the training set was perfectly predictive of whether the fruit would be a tangerine or orange.

We can imagine that this would not hold true across the country or the world, which would produce a situation where the training set accuracy would be perfect, but the validation set accuracy would not be able to approach that using the same methods.

Yes, it is possible that validation accuracy cannot be as high as training accuracy. Any situation in which noise (as opposed to generalizable properties of the feature set) in the training set is more predictive of the target variable within the training set would produce this.

Consider a situation in which a random property of the sample considered is perfectly predictive, but this is found not to be true of all other examples outside the sample. The predictive power in the training set would be perfect, but outside the training set, less than perfect.

Yes, it is possible that validation accuracy cannot be as high as training accuracy. Any situation in which noise (as opposed to generalizable properties of the feature set) in the training set is more predictive of the target variable within the training set would produce this.

Consider a situation in which a random property of the training sample considered is perfectly predictive, but this is found not to be true of all other examples outside the sample. The predictive power in the training set would be perfect, but outside the training set, less than perfect.

For example, let's consider a case where you have a set of fruit data and you're trying to establish whether a given fruit is an orange or a tangerine. You have 4 features- the circumference of the fruit around the stem-medial axis, the height of the fruit across the stem, a numerical value of the hue of the fruit skin, and the first letter of the amateur baseball team whose home park is closest to the field the fruit was grown in. Let's imagine that by some baffling coincidence, the baseball team letter in the training set was perfectly predictive of whether the fruit would be a tangerine or orange.

We can imagine that this would not hold true across the country or the world, which would produce a situation where the training set accuracy would be perfect, but the validation set accuracy would not be able to approach that using the same methods.