Introducere în învățarea automată (machine learning)
Introducere
De ce ar trebui să învețăm despre învățarea automată Click to read ![](./images/book.png)
Statistica și învățarea automată oferă principalele instrumente pentru munca unui cercetător în domeniul datelor. Înțelegerea diferitelor metode de învățare automată - cum funcționează, care sunt principalele lor avantaje și cum se evaluează performanța lor - vă poate ajuta să luați decizii mai bune referitoare la când să le utilizați și oferă versatilitate în analiza datelor.
IA, ML, DL și știința datelor
Definiții Click to read ![](./images/book.png)
➢IA: Inteligența artificială
➢ML: Machine Learning (învățarea automată)
➢DL: Deep Learning (învățarea profundă)
➢DS: Data Science (știința datelor)
IA: Inteligența artificială
Un sistem informatic care poate îndeplini sarcini asociate în mod normal cu inteligența umană
De exemplu: percepția vizuală, recunoașterea vorbirii, luarea deciziilor, traducerea textelor
IA generală vs. IA restrânsă:
IA generală poate gestiona toate sarcinile, IA restrânsă doar un subset selectat de sarcini. În prezent, nu există un sistem de IA care să poată fi considerat IA general, iar dacă un astfel de sistem va putea exista în viitor este o chestiune intens dezbătută.
IA: Inteligența artificială
Un sistem informatic care poate îndeplini sarcini asociate în mod normal cu inteligența umană
de exemplu: percepția vizuală, recunoașterea vorbirii, luarea deciziilor, traducerea între limbi
Dacă algoritmul...
... constă în instrucțiuni ce trebuie parcurse pas cu pas pentru a realiza activitatea
atunci este IA simbolică
... detectează un model bazat pe un volum mare de date/exemple și utilizează acest model pentru a realiza activitatea
atunci este învățarea automată
ML: Machine Learning (învățare automată)
În loc să programeze fiecare pas, o mașină este programată pentru a găsi modele și „învață” pe baza exemplelor. Există o zonă mare de suprapunere cu Statistica.
➢Salvează mii de linii de cod
➢Flexibilă:
dacă datele se modifică,
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programul se adaptează.
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ML: Machine Learning
În loc să programeze fiecare pas, o mașină este programată pentru a găsi modele și „învață” pe baza exemplelor. Există o zonă mare de suprapunere cu Statistica.
➢AI: Inteligenţă artificială
Un sistem informatic care poate îndeplini sarcini ce necesită în mod normal inteligență umană
➢ML: Machine Learning
În loc să programeze fiecare pas, o mașină este programată pentru a găsi modele și „învață” pe baza exemplelor. Există o zonă mare de suprapunere cu Statistica.
➢DS: Data Science (Știința datelor)
Folosește metode din Machine Learning și Statistică pentru a rezolva probleme și a lua decizii pe baza datelor.
➢DL: Deep Learning (învățarea profundă)
În prezent, una dintre cele mai populare și de succes metode de învățare automată. Bazat pe algoritmi de rețele neuronale cu mai multe straturi (vezi Unitatea 2, Secțiunea 5).
➢ Salvează mii de linii de cod
➢ Flexibil: dacă datele se modifică, programul se adaptează.
➢ Rezolvă probleme pentru care nu avem o soluție pas cu pas: de ex. recunoașterea imaginii și a sunetului
➢ Joacă jocuri și ne bate
Tipuri de machine learning (învățare automată) Click to read ![](./images/book.png)
Algoritmii de învățare automată pot fi subdivizați în trei tipuri diferite.
➢Învățare supervizată (supervised learning)
➢ Învățare nesupervizată (unsupervised learning)
➢ Învățare prin recompensă (reinforcement learning)
➢ Învățarea supervizată (supervised learning)
În învățarea supervizată, un algoritm primește „date etichetate” ca intrare “date de antrenament”: datele etichetate sunt seturi de date în care fiecare instanță (exemplu) constă din variabile independente și valoarea variabilei țintă care ar trebui să fie determinată pe baza variabilelor independente. Cu alte cuvinte, aceasta este pur și simplu învățare prin exemplu. Scopul algoritmului este de a detecta un model care îi va permite să determine corect valoarea variabilei țintă, atunci când variabilele independente sunt cunoscute.
Învățarea supravegheată se numește clasificare, atunci când variabila țintă este o categorie – de exemplu, stabilirea dacă un solicitant de împrumut prezintă sau nu un risc ridicat, pe baza istoricului demografic și anterior de creditare. Se numește regresie atunci când variabila țintă este continuă – de exemplu, prezicerea creșterii PIB-ului unei națiuni pe baza factorilor economici actuali.
➢ Învățare nesupervizat
Învățarea nesupervizată este utilizată pentru a detecta tipare în datele neetichetate. Clustering (încercarea de a determina grupuri similare în date, fără a ști a priori ce grupuri să caute) sau metodele de reducere a dimensiunii (cum ar fi analiza componentelor principale sau LDA) sunt cele mai populare tipuri de învățare nesupravegheată.
➢Învățare prin recompensă (Reinforcement learning - RL)
Învățarea prin recompensă este utilizată pentru a identifica o strategie optimă în situațiile în care agentul algoritmic este obligat să interacționeze cu un mediu dat și să ia o serie de decizii, înainte ca rezultatul final să fie cunoscut (adică feedback-ul nu este imediat: succes vs eșec, câștig vs pierde). Metodele RL sunt cel mai frecvent utilizate în jocuri sau în conducerea autonomă și pentru mișcarea roboților.
Înainte de a trece la examinarea diferitelor tipuri de algoritmi de învățare automată, un cuvânt pentru înțelepți
➢învățarea automată este un subiect empiric
➢a priori, de obicei nu există „o metodă corectă de aplicat”
➢va trebui să:
➢colectați cerințele pentru cazul particular de utilizare,
➢încercați câteva metode diferite (Unitatea 2),
➢și apoi să le evaluați în funcție de valorile alese (Unitatea 3) și cerințele (de exemplu, poate că acuratețea nu este singurul factor important, ci și explicabilitatea),
➢pentru a determina care metodă funcționează „cel mai bine”.
Algoritmi de învățare automată
Naive Bayes Click to read ![](./images/book.png)
Naïve Bayes este un algoritm de clasificare simplu, care este adesea folosit ca referință în procesarea limbajului natural.
Naïve Bayes folosește teorema lui Bayes pentru a transforma problema determinării probabilității unei instanțe aparținând clasei Y, având în vedere atributele sale xi, în problema evaluării frecvenței atributului xi, având în vedere că instanța aparține clasei Y.
Poate oferi rezultate bune atunci când:
➢toate atributele sunt la fel de importante în determinarea clasei țintă,
➢pentru o clasă țintă anume, atributele sunt independente reciproc.
Naive Bayes are diferite variante:
➢Gaussian NB: folosit atunci când variabilele atribut sunt numerice și se poate presupune că urmează o distribuție gaussiană
➢Simplu NB: folosit când variabilele atribut sunt categoriale
➢Multi-nomial NB: cel mai adesea folosit în contexte de procesare a limbajului natural, unde atributele sunt cuvinte dintr-un document.
Arbori de decizie Click to read ![](./images/book.png)
Arborii de decizie reprezintă atât o modalitate de reprezentare a informațiilor, cât și un algoritm pentru detectarea modelelor în date.
Aici ne concentrăm pe algoritmul de detectare a șabloanelor, dar reprezentăm și informațiile obținute în proces folosind un arbore de decizie.
➢Arborele de decizie este compus din noduri și ramuri.
➢Fiecare nod este un atribut și are ramuri în funcție de valorile sale. De exemplu, dacă un atribut este „an în facultate” și valorile posibile ale atributului sunt (Boboc, Anul 2, Junior, Senior), atunci nodul corespunzător „an în facultate” ar avea 4 ramuri.
➢Arborii de decizie sunt parcurși de la nodul rădăacină în jos: la fiecare nod, trebuie luată o decizie cu privire la ce ramură trebuie urmată în continuare, pe baza valorii (valorilor) unui anumit atribut (atribute).
Random forests Click to read ![](./images/book.png)
Random forests reprezintă un tip de „învățare de ansamblu” – o clasă de metode care combină modele pentru a îmbunătăți acuratețea predictivă prin diversitate.
➢Random forests constau într-o serie de arbori de decizie aleși aleatoriu și
➢combină previziunile lor
➢Random forests variază în funcție de numărul de copaci și de adâncimea fiecărui copac.
Rețele neuronale Click to read ![](./images/book.png)
O rețea neuronală constă dintr-o serie de unități interconectate (așa-numiții „neuroni”), precum cea prezentată în figura din dreapta.
Fiecare neuron preia mai multe intrări, atribuie fiecărei intrări o pondere, apoi le combină și le rulează printr-o funcție de activare, pentru a produce o singură ieșire.
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O rețea neuronală se formează prin organizarea așa-numiților neuroni în straturi.
Antrenarea unei rețele neuronale reprezintă încercarea de a stabili valorile pentru ponderile rețelei care minimizează eroarea de predicție a datelor de antrenament (măsurată de o funcție de pierdere dată).
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Evaluarea performanței
Acuratețea și alte metrici Click to read ![](./images/book.png)
Există diverse metrici care pot fi utilizate pentru măsurarea performanței unui model antrenat. Selectarea metricii depinde de tipul modelului (învățare supervizată, nesupervizată sau prin recompensă; clasificare vs regresie) și de contextul de utilizare. Ne vom concentra pe învățarea supervizată.
➢Cea mai frecvent utilizată măsură de performanță pentru modelele de regresie este MSE.
➢Cea mai des folosită măsură de performanță pentru modelele de clasificare este acuratețea.
➢Cu toate acestea, acestea nu sunt întotdeauna cele mai bune metrici de utilizat.
Clasificatorii binari sunt sisteme de clasificare în care există doar două clase țintă posibile: POZITIV si NEGATIV.
Vom examina diferite valori de performanță pentru acestea și de ce, în anumite circumstanțe, sunt preferabile acurateței.
Să începem cu un instrument utilizat în mod obișnuit pentru a ajuta la înțelegerea performanței unui clasificator binar: matricea de confuzie (vezi slide-ul următor).
Când este indicat să fie utilizată o altă metrică decât acuratețea?
➢Când clasele țintă sunt sever dezechilibrate: de exemplu, dacă 95% sunt valori POZITIVE, și doar 5% sunt NEGATIVE, atunci un clasificator care pur și simplu a clasificat totul ca fiind POZITIV ar avea o precizie uimitoare de 95%. Dar, la ce ar folosi?
➢Este mai important să se identifice corect toate valorile POZITIVE (de exemplu, într-un diagnostic medical, vrem să ne asigurăm că surprindem prezența unei boli, pentru a putea începe tratamentul)? Sau este mai important să evităm estimările fals POZITIVE?
Să luăm în considerare un exemplu „real”: detectarea automată a discursului instigator la ură pe rețelele sociale.
Conform datelor obținute în cadrul proiectului „Barometro dell‘Odio” al Amnesty International Italia (a se vedea prezentarea data4good), discursul instigator la ură reprezintă aproximativ 1% din conținutul politic online. În acest caz, chiar și pentru un sistem de detectare a discursului instigator la ură cu 99% TPR și 1% FPR:
For every 100 comments that it labels as hate speech, we can expect ___ of them to actually be neutral comments.
Lets consider a „real-life“ example: automated hate speech detection on social media.
According to data obtained in Amnesty International Italy‘s „Barometro dell‘Odio“ project (see the data4good slides), hate speech makes up about 1% of online political content. In that case, even for a stellar hate speech detection system with 99% TPR and 1% FPR:
Pentru fiecare 100 de comentarii pe care le etichetează drept discurs instigator la ură, ne putem aștepta ca ___ dintre ele să fie de fapt comentarii neutre.
Nu mă crede pe cuvânt
Următorul slide are o foaie Excel încorporată - vă puteți juca cu numerele.
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)
Bootstrapping și validare încrucișată Click to read ![](./images/book.png)
Bootstrapping:
Bootstrapping-ul se bazează pe efectuarea unei eșantionări aleatorii cu înlocuire (adică se ia vaza cu bile colorate, atât de frecvente în textele cu probabilități, încât se trage aleatoriu o minge, se notează culoarea și se aruncă mingea înapoi în vază) pe datele de antrenament. Aceasta înseamnă că aceeași observație poate fi trasă de mai multe ori, în timp ce alte observații pot să nu fie trase deloc.
Acest procedeu statistic este exploatat: eșantioanele sunt extrase din datele de antrenament de câte ori este necesar, până când se obține un nou set de date de antrenament de aceeași dimensiune. Acele observații care nu au fost niciodată trase în această procedură sunt introduse în setul de date de validare. Rezultatele validării sunt folosite pentru a compara diferiți algoritmi.
Validare încrucișată:
Există diferite moduri de a efectua validarea încrucișată, dar ne concentrăm pe validarea încrucișată de n ori și setăm n = 5 pentru simplitate.
Setul de date de antrenament este împărțit, prin eșantionare aleatorie, în 5 subgrupuri de dimensiuni aproximativ egale.
➢La prima trecere, se folosește grupul de date 1 ca date de validare și datele rămase (grupurile 2,3,4,5) sunt date de antrenament.
➢La a doua trecere, al doilea grup de date este pus deoparte pentru validare, iar algoritmul se antrenează pe celelalte grupuri de date (1,3,4,5).
➢Se continuă în acest fel până când toate cele 5 grupuri de date au servit drept date de validare o singură dată.
➢Se folosesc apoi 5 metrici de validare (de exemplu, rata de eroare pentru clasificare, MSE pentru regresie) care pot fi folosite pentru a compara diferiți algoritmi.
➢Odată ce validarea este finalizată și a fost selectat „cel mai bun” model, acesta poate fi reantrenat pe întregul set de date.
Considerații suplimentare Click to read ![](./images/book.png)
Privind doar acuratețea modelului uneori nu este suficientă.
Luați în considerare următorul exemplu.
Multe sisteme AI sunt folosite pentru clasificare. Aici, de exemplu, pentru clasificarea imaginilor.
După multe exemple, algoritmul a „învățat”...
![](https://datascience-project.eu/private-data/ckeditor/plugins/imageuploader/uploads/828370c2.jpg)
După multe exemple, algoritmul a învățat...
It does not detect wolf vs. dog – it detects snow vs. no snow!
![](https://datascience-project.eu/private-data/ckeditor/plugins/imageuploader/uploads/8385288f.jpg)
Privind doar acuratețea modelului uneori nu este suficient.
Pentru multe aplicații, este, de asemenea, necesar să înțelegem ce șabloane a detectat algoritmul și ce variabile au condus la rezultatul modelului. Transparența și explicabilitatea pot fi caracteristici importante de „calitate”, iar acest lucru poate duce și la alegerea modelului care funcționează „cel mai bine”.
În plus, în fișierul data4good, sunt prezentate alte criterii, cum ar fi corectitudinea și nediscriminarea, care pot fi caracteristici importante atunci când se evaluează performanța unui model.
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