Results for tuple translation from English to Persian

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English

tuple .

Persian

چند تائي .

Last Update: 2011-10-24
Usage Frequency: 1
Quality:

English

n tuple .

Persian

جائي .

Last Update: 2011-10-24
Usage Frequency: 1
Quality:

English

the value "n" is called the arity of the tuple.

Persian

the value "n" is called the arity of the tuple.

Last Update: 2016-03-03
Usage Frequency: 1
Quality:

Warning: Contains invisible HTML formatting

English

a "tuple" represents a collection of "n" values, where "n" ≥ 0.

Persian

a "tuple" represents a collection of "n" values, where "n" ≥ 0.

Last Update: 2016-03-03
Usage Frequency: 1
Quality:

Warning: Contains invisible HTML formatting

English

a 3-tuple would be represented as codice_8, where a, b and c are values of possibly different types.

Persian

a 3-tuple would be represented as codice_8, where a, b and c are values of possibly different types.

Last Update: 2016-03-03
Usage Frequency: 1
Quality:

English

one tuple will represent the beginning of the range, marked with type −1 as and the other will represent the end with type +1 as .

Persian

یک چندتایی شروع محدوده را بیان خواهد کرد، با نوع ۱- مانند نشان داده می‌شود و دیگری پایان را با نوع ۱+ مانند بیان خواهد کرد.

Last Update: 2016-03-03
Usage Frequency: 1
Quality:

Warning: Contains invisible HTML formatting

English

2. why is tree pruning useful in decision tree induction? what is a drawback of using a separate set of tuples to evaluate pruning? answer: the decision tree built may overfit the training data. there could be too many branches, some of which may reflect anomalies in the training data due to noise or outliers. tree pruning addresses this issue of overfitting the data by removing the least reliable branches (using statistical measures). this generally results in a more compact and reliable decisi

Persian

2. چرا هرس درختان در القای درخت تصمیم مفید است؟ استفاده از مجموعه جداگانه تاپل ها برای ارزیابی هرس چه اشکالی دارد؟ پاسخ: درخت تصمیم ساخته شده ممکن است بیش از حد با داده های آموزشی مطابقت داشته باشد. ممکن است شاخه های زیادی وجود داشته باشد که برخی از آنها ممکن است ناهنجاری هایی را در داده های آموزشی به دلیل نویز یا نقاط پرت منعکس کنند. هرس درختان با حذف کمترین انشعابات قابل اعتماد (با استفاده از معیارهای آماری) به این موضوع مربوط به بیش از حد برازش داده ها می پردازد. این به طور کلی منجر به تصمیم گیری فشرده تر و قابل اعتمادتر می شود

Last Update: 2021-10-26
Usage Frequency: 1
Quality:

Reference: Anonymous

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