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المقياس: Apprentissage automatique II

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What's the difference between Supervised vs Unsupervised Learning

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نشر على 19:34, الخميس 31 أكت 2019 By Imed BOUCHRIKA
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Dans l'apprentissage supervisé Ilya un superviseur qui donne les sorties désiré au algorithme mais apprentissage non supervisé il n'est pas un superviseur et les sorties désiré 

نشر على 21:01, الخميس 31 أكت 2019 by Aymen Chabi (1 points)
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for the supervised learning, the training/test data is given with desired ouputs se we can build our model based on this data, for example the classification algorithms is under the supervised learning. Otherwise on the unsupervised learning we don't give the desired outputs, like the clustring algorithms.

نشر على 11:15, الجمعة 1 نوف 2019 by abdennour redjaibia (282 points)
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Supervised learning : is simply a process of learning algorithm. he is where you have input variables and an output variable and you use an algorithm to learn the mapping function from the input to the output.

The aim is to approximate the mapping function so that when we have new input data we can predict the output variables for that data.

Unsupervised learning : is modeling the underlying or hidden structure or distribution in the data in order to learn more about the data. he is where you only have input data and no corresponding output variables.

نشر على 21:16, الجمعة 1 نوف 2019 by Mouhamed Salah Ben khalfallah (14 points)
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Supervised learning is where you have input variables (x) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output. Y = f(X)

The goal is to approximate the mapping function so well that when you have new input data (x) that you can predict the output variables (Y) for that data.

Unsupervised learning is where you only have input data (X) and no corresponding output variables.

The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data.

نشر على 13:56, السبت 2 نوف 2019 by khalil souaiaia (13 points)
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  • Supervised learning : - Input variables and output variables(desired outputs) will be given.

                                              - To determine the function so that when new input data set given , we can predict the output.

                                              - Examples : * Classification(Discrete outputs).

                                                                   * Regression(Continuous outputs).

  • Unsupervised learning : - Only input data will be given (without output desired).

                                                  - To model the hidden patterns and to explore data .

                                                  - Examples: * Clustering (Kmeans) .

                                                                      * Association: (Apriori algorithm).                                                

نشر على 21:08, السبت 2 نوف 2019 by Fateh Ben khalfallah (6 points)
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supervised learning: input variables and output variables(training with results).

unsupervised learning: input only data without outputs.

نشر على 15:53, الجمعة 15 نوف 2019 by belkis maarfia (25 points)
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Supervised learning is when the model is trained on a labelled dataset. A Labelled dataset is one which has both the input and output parameters.

unsupervised learning is when the model is trained on an unlabelled dataset.

نشر على 19:16, الخميس 12 ديس 2019 by noussaiba ledjemel (23 points)
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جواب (8)

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well the supervised learning All data is labeled and the algorithms learn to predict the output from the input data.
but the unsupervised All data is unlabeled and the algorithms learn to inherent structure from the input data.

نشر على 20:40, الثلاثاء 17 ديس 2019 by abd raouf ben tlidjane (9 points)
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جواب (9)

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supervised learning is technique deal with labelled data where the out data patterns

are known to the system.

supervised learning :

  •   uses known and labeled input data 
  • very complex in computation 
  • uses off-time  analyses 
  • number of class is known and the accurate and reliable results.

unsupervised learning:works with unlabeled data in which the output is just based on the collection of perception .

unsupervised learning:

  • uses unknown input data 
  • less computational
  • uses real time analysis of data 
  • number of classes is not known 
  • moderate accurate and reliable results .

        

نشر على 21:30, الجمعة 3 ينا 2020 by sihem djélamda (9 points)
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